Classification of Remote Sensing

地统计以及遥感英文词汇300个：gray level co-occurrence matrix algorithm灰度共生矩阵算法characteristic of atmospheric transmission 大气传输特性earth resources technology satellite,ERTS 地球资源卫星Land-use and land-over change 土地利用土地覆盖变化Multi-stage stratified random sample 多级分层随机采样Normalized Difference Vegetation Index归一化植被指数Soil-Adjusted Vegetation Index土壤调整植被指数Modified Soil-Adjusted Vegetation Index修正土壤调整植被指数image resolution ,ground resolution影象分辨力(又称“象元地面分辨力”。

指象元地面尺寸。

) remote sensing information transmission遥感信息传输remote sensing information acquisition遥感信息获取multi- spectral remote sensing technology多光谱遥感技术Availability and accessibility 可用性和可获取性Association of Geographic Information (AGI) 地理信息协会Difference Vegetation Index差值植被指数image quality 影象质量Enhanced Vegetation Index增强型植被指数Ratio Vegetation Index比值植被指数Spatial autocorrelation 空间自相关Lag Size 滞后尺寸Ordinary kriging 普通克里金Indicator kriging 指示克里金Disjunctive kriging 析取克里金Simple kriging 简单克里金Bivariate normal distributions 双变量正态分布Universal kriging 通用克里金conditional simulation 条件模拟image filtering 图像滤波optimal sampling strategy 最优采样策略temporal and spatial patterns 时空格局Instantaneous field-of-view瞬时视场角azimuth 方位角wavelet transform method 小波变换算法priori probability 先验概率geometric distortion 几何畸变active remote sensing主动式遥感passive remote sensing 被动式遥感multispectral remote sensing多谱段遥感multitemporal remote sensing 多时相遥感infrared remote sensing 红外遥感microwave remote sensing微波遥感quantizing，quantization量化sampling interval 采样间隔digital mapping数字测图digital elevation model,DEM 数字高程模型digital surface model,DSM 数字表面模型solar radiation spectrum太阳辐射波谱atmospheric window 大气窗atmospheric transmissivity大气透过率atmospheric noise 大气噪声atmospheric refraction 大气折射atmospheric attenuation 大气衰减back scattering 后向散射annotation 注解spectrum character curve 波谱特征曲线spectrum response curve 波谱响应曲线spectrum feature space波谱特征空间spectrum cluster 波谱集群infrared spectrum 红外波谱reflectance spectrum反射波谱electro-magnetic spectrum 电磁波谱object spectrum characteristic地物波谱特性thermal radiation 热辐射microwave radiation微波辐射data acquisition数据获取data transmission数据传输data processing 数据处理ground receiving station地面接收站environmental survey satellite环境探测卫星geo-synchronous satellite地球同步卫星sun-synchronous satellite太阳同步卫星satellite attitude卫星姿态remote sensing platform 遥感平台static sensor 静态传感器dynamic sensor动态传感器optical sensor光学传感器microwave remote sensor微波传感器photoelectric sensor光电传感器radiation sensor辐射传感器satellite-borne sensor星载传感器airborne sensor机载传感器attitude-measuring sensor 姿态测量传感器image mosai图象镶嵌c image digitisation图象数字化ratio transformation比值变换biomass index transformation生物量指标变换tesseled cap transformation 穗帽变换reference data 参照数据image enhancement 图象增强edge enhanceme边缘增强ntedge detection边缘检测contrast enhancement反差增强texture enhancement 纹理增强ratio enhancement 比例增强texture analysis 纹理分析color enhancement 彩色增强pattern recognition 模式识别classifier 分类器supervised classification监督分类unsupervised classification非监督分类box classifier method 盒式分类法fuzzy classifier method 模糊分类法maximum likelihood classification最大似然分类minimum distance classification最小距离分类Bayesian classification 贝叶斯分类Computer-assisted classification机助分类illumination 照度principal component analysis 主成分分析spectral mixture analysis 混合像元分解fuzzy sets 模糊数据集topographic correction 地形校正ground truth data 地面真实数据Tasselled cap 缨帽变换Artificial neural networks 人工神经网络Visual interpretation 目视解译accuracy assessment 精度评价Omission error漏分误差commission error 错分误差Multi-source data 多源数据heterogeneous 非均质的Training sample 训练样本ancillary data 辅助数据dark-object subtraction 暗目标相减法discriminant analysis 判别分析‘salt and pepper’ effects 椒盐效应spectral confusion光谱混淆Cluster sampling 聚簇采样systematic sampling 系统采样Error matrix误差矩阵hard classification 硬分类Soft classification 软分类decision tree classifier 决策树分类器Spectral angle classifier 光谱角分类器support vector machine支持向量机Fuzzy expert system 模糊专家系统endmember spectral端元光谱Future extraction 特征提取image mosaic图像镶嵌density slicing密度分割least squares correlation 最小二乘相关data fusion 数据融合Image segmentation图像分割urban remote sensing 城市遥感atmospheric remote sensing大气遥感geomorphological remote sensing地貌遥感ground resolution地面分辨率ground date processing system地面数据处理系统ground remote sensing地面遥感object spectrum characteristic地物波谱特性space characteristic of object地物空间特性geological remote sensing地质遥感multispectral remote sensing多光谱遥感optical remote sensing technology光学遥感技术ocean remote sensing海洋遥感marine resource remote sensing海洋资源遥感aerial remote sensing航空遥感space photography航天摄影space remote sensing航天遥感infrared remote sensing红外遥感infrared remote sensing technology红外遥感技术environmental remote sensing环境遥感laser remote sensing激光遥感polar region remote sensing极地遥感visible light remote sensing可见光遥感range resolution空间分辨率radar remote sensing雷达遥感forestry remote sensing林业遥感agricultural remote sensing农业遥感forest remote sensing森林遥感water resources remote sensing水资源遥感land resource remote sensing土地资源遥感microwave emission微波辐射microwave remote sensing微波遥感microwave remote sensing technology微波遥感技术remote sensing sounding system遥感测深系统remote sensing estimation遥感估产remote sensing platform遥感平台satellite of remote sensing遥感卫星remote sensing instrument遥感仪器remote sensing image遥感影像remote sensing cartography遥感制图remote sensing expert system遥感专家系统active remote sensing主动式遥感passive remote sensing被动式遥感resource remote sensing资源遥感ultraviolet remote sensing紫外遥感attributive geographic data 属性地理数据attributes, types 属性，类型Geographic database types 地理数据库类型attribute data 属性数据Geographic individual 地理个体Geographic information (GI) 地理信息Exponential transform指数变换false colour composite 假彩色合成Image recognition 图像识别image scale 图像比例尺Spatial frequency 空间频率spectral resolution 光谱分辨率Logarithmic transform对数变换mechanism of remote sensing 遥感机理adret 阳坡beam width波束宽度biosphere生物圈curve fitting 曲线拟合geostationary satellite对地静止卫星glacis缓坡Field check 野外检查grating 光栅gray scale 灰阶Interactive 交互式interference干涉inversion 反演Irradiance 辐照度landsatscape 景观isoline 等值线Lidar激光雷达landform analysis地形分析legend 图例Map projection地图投影map revision地图更新Middle infrared中红外Mie scattering 米氏散射opaco 阴坡orbital period 轨道周期Overlap重叠parallax 视差polarization 极化Phase 相位pattern 图案quadtree象限四分树Radar returns雷达回波rayleigh scattering 瑞利散射reflectance 反射率Ridge山脊saturation 饱和度solar elevation太阳高度角Subset 子集telemetry遥测surface roughness表面粗糙度Thematic map专题制图thermal infrared热红外uniformity均匀性Upland 高地vegetal cover 植被覆盖watershed流域White plate白板zenith angle天顶角radiant flux 辐射通量Aerosol 气溶胶all weather 全天候angle of field 视场角Aspect 坡向atmospheric widow大气窗口atmospheric 大气圈Path radiance 路径辐射binary code二进制码black body 黑体Cloud cover云覆盖confluence 汇流点diffuse reflection漫反射Distortion畸变divide分水岭entropy熵meteosat气象卫星bulk processing粗处理precision processing精处理Bad lines 坏带single-date image单时相影像Decompose 分解threshold 阈值relative calibration 相对校正post-classification 分类后处理Aerophotograph 航片Base map 底图muti-temporal datasets 多时相数据集detector 探测器spectrograph 摄谱仪spectrometer 波谱测定仪Geostatistics 地统计Semivariogram 半方差sill 基台Nugget 块金Range 变程Kriging 克里金CoKriging 共协克里金Anisotropic 各向异性Isotropic 各向同性scale 尺度regional variable 区域变量transect 横断面Interpolation 插值heterogeneity 异质性texture 纹理digital rectification数字纠正digital mosaic 数字镶嵌image matching影像匹配density 密度grey level灰度pixel,picture element 象元target area目标区searching area 搜索区Spacelab 空间实验室space shuttle航天飞机Landsat陆地卫星Seasat 海洋卫星Mapsat测图卫星Stereosat 立体卫星aspatial data 非空间数据。

风速不确定度的分析和计算马友林【期刊名称】《风能》【年(卷),期】2018(000)008【总页数】14页(P102-115)【作者】马友林【作者单位】鉴衡认证中心【正文语种】中文本文主要描述了IEC 61400-12-1 Annex D 和Annex E中不确定度（Uncertainty）相关部分的处理计算，主要包含以下三个部分：（1）不确定度的基本概念。

（2）IEC 61400-12-1不确定度介绍。

（3）IEC 61400-12-1风速测试不确定度。

本文主要遵循以下标准的相关内容（下称“标准”）：IEC 61400-12-1:2017, Wind turbine-Part 12-1: Power performance measurements of electricity producing wind turbines。

不确定度的基本概念测量不确定度意指根据所用到的信息，表征赋予被测量值分散性的非负参数。

测试不确定度是基于被测量的概率分布而估计的离散程度，用标准差表示的不确定度又被称为标准不确定度。

在某些情况下需要对不确定度进行更可靠的估计，即对标准不确定度乘以一个包含因子k得到扩展不确定度。

不确定度分为A、B两类，它们的本质是一样的，其区别在于获取被测量概率分布的方式不同；不能简单地用“系统误差”和“随机误差”进行区分。

A类不确定度：其概率分布是直接的，通过多次测量的方式得到的。

B类不确定度：其概率分布是间接的，通过数学模型、假定等方式得到的。

将各不确定度分量进行合并，可以得到合成不确定度。

测量模型为：敏感系数为：如果各分量不相关：式中：U(xi)为不确定度分量。

如果分量相关：[注：不确定度的概念和计算主要参考以下标准：JJF1059.1-2012、JCGM 100: 2018 (GUM1995 with minor corrections)]。

风速不确定度一、概述在测试中需要使用传感器采集风速相关数据，针对不同传感器，其不确定度的计算也有所差异。

1遥感remote sensing2资源与环境遥感remote sensing of natural resources and environment 3主动式遥感active remote sensing4被动式遥感passive remote sensing5多谱段遥感multispectral remote sensing6多时相遥感multitemporal remote sensing7红外遥感infrared remote sensing8微波遥感microwave remote sensing9太阳辐射波谱solar radiation spectrum10大气窗atmospheric window11大气透过率atmospheric transmissivity12大气噪声atmospheric noise13大气传输特性characteristic of atmospheric transmission14波谱特征曲线spectrum character curve15波谱响应曲线spectrum response curve16波谱特征空间spectrum feature space17波谱集群spectrum cluster18红外波谱infrared spectrum20反射波谱reflectance spectrum21电磁波谱electro-magnetic spectrum功率谱power spectrum33地物波谱特性object spectrum characteristic4热辐射thermal radiation35微波辐射microwave radiation36数据获取data acquisition37数据传输data transmission38数据处理data processing39地面接收站ground receiving station40数字磁带digital tape41模拟磁带analog tape42计算机兼容磁带computer compatible tape，CCT43高密度数字磁带high density digital tape， HDDT44图象复原image restoration45模糊影象fuzzy image46卫星像片图satellite photo map47红外图象infrared imagery48热红外图象thermal infrared imagery， thermal IR imagery49微波图象microwave imagery350成象雷达imaging radar351熵编码entropy coding352冗余码redundant code353冗余信息redundant information354信息量contents of information355信息提取information extraction356月球轨道飞行器lunar orbiter357空间实验室Spacelab358航天飞机space shuttle359陆地卫星Landsat360海洋卫星Seasat361测图卫星Mapsat362立体卫星Stereosat363礼炮号航天站Salyut space station364联盟号宇宙飞船Soyuz spacecraft365SPOT卫星SPOT satellite，systeme probatoire d’observation de la terse（法）366地球资源卫星earth resources technology satellite， ERTS367环境探测卫星environmental survey satellite368地球同步卫星geo－synchronous satellite369太阳同步卫星sun－synchronous satellite370卫星姿态satellite attitude371遥感平台remote sensing platform372主动式传感器active sensor373被动式传感器passive sensor374推扫式传感器push－broom sensor375静态传感器static sensor376动态传感器dynamic sensor377光学传感器optical sensor378微波传感器microwave remote sensor379光电传感器photoelectric sensor380辐射传感器radiation sensor381星载传感器satellite－borne sensor382机载传感器airborne sensor383姿态测量传感器attitude－measuring sensor384探测器detector385摄谱仪spectrograph386航空摄谱仪aerial spectrograph387波谱测定仪spectrometer388地面摄谱仪terrestrial spectrograPh389测距雷达range－only radar390微波辐射计microwave radiometer391红外辐射计infrared radiometer392侧视雷达side－looking radar, SLR393真实孔径雷达real－aperture radar394合成孔径雷达synthetic aperture radar， SAR395专题测图传感器thematic mapper， TM396红外扫描仪infrared scanner397多谱段扫描仪multispectral scanner． MSS398数字图象处理digital image processing399光学图象处理optical image processing400实时处理real－time processing401地面实况ground truth402几何校正geometric correction403辐射校正radiometric correction404数字滤波digital filtering405图象几何配准geometric registration of imagery406图象几何纠正geometric rectification of imagery407图象镶嵌image mosaic408图象数字化image digitisation409彩色合成仪additive colir viewer410假彩色合成false color composite411直接法纠正direct scheme of digital rectification 412间接法纠正indirect scheme of digital rectification 413图象识别image recognition414图象编码image coding415彩色编码color coding416多时相分析multitemporal analysis417彩色坐标系color coordinate system418图象分割image segmentation419图象复合image overlaying420图象描述image description421二值图象binary image422直方图均衡histogram equalization423直方图规格化histogram specification424图象变换image transformation425彩色变换color transformation426伪彩色pseudo－color427假彩色false color428主分量变换principal component transformation 429阿达马变换Hadamard transformation430沃尔什变换Walsh transformation431比值变换ratio transformation4生物量指标变换biomass index transformation4 穗帽变换tesseled cap transformation4 4参照数据reference data435图象增强image enhancement436边缘增强edge enhancement437边缘检测edge detection438反差增强contrast enhancement439纹理增强texture enhancement440比例增强ratio enhancement441纹理分析texture analysis442彩色增强color enhancement443模式识别pattern recognition444特征feature445特征提取feature extraction446特征选择feature selection447特征编码feature coding448距离判决函数distance decision function449概率判决函数probability decision function450模式分析pattern analysis451分类器classifier452监督分类supervised classification453非监督分类unsupervised classification454盒式分类法box classifier method455模糊分类法fuzzy classifier method456最大似然分类maximum likelihood classification 457最小距离分类minimum distance classification 458贝叶斯分类Bayesian classification459机助分类computer－assisted classification460图象分析image analysis。

dert分类算法
DERT分类算法是一种基于深度学习的遥感图像分类算法，全称为遥感图像深度增强分类算法（Deep Enhanced Remote Sensing Image Classification Algorithm）。

DERT算法主要分为三个步骤：
１．预处理：对遥感图像进行预处理，包括灰度化、噪声去除、图像增强等操作，以提高图像的清晰度和分类精度。

２．特征提取：利用深度学习技术对预处理后的图像进行特征提取，包括卷积层、池化层、全连接层等操作，以提取出图像中的有用特征。

３．分类器设计：根据提取出的特征设计分类器，包括支持向量机、神经网络等分类器，以实现遥感图像的分类。

DERT算法具有较高的分类精度和鲁棒性，能够有效地应用于遥感图像分类领域。

1.remote sensing used in forestry 林业遥感2.restoration of natural resources 自然资源的恢复3.above ground biomass（AGB）地上生物量4.biogeochemical cycle 生物地球化学循环5.carbon cycle 碳循环6.stand structure 林分结构7.high deforestation rates 森林砍伐率8.carbon emissions 碳排放9.environmental degradation 环境恶化10.biomass estimation 生物量估测11.field data 样地数据12.remotely sensed data 遥感数据13.statistical relationships 统计相关性14.biomass estimation model 生物量估测模型15.stem diameter 径阶16.stem height 枝下高17.Tree height树高18.Primary Forest 原始森林19.Successional Forest 次生林20.Endmember 端元21.canopy shadow冠层阴影22.canopy closure 冠层郁闭度23.sampling strategy 抽样方案24.stratified random 分层随机25.endmembers 端元26.intrinsic dimensionality 固有维数27.phenological changes 物候变化28.Chlorophyll 叶绿素29.Absorption 吸收30.Amplitude 振幅31.spatial frequency 空间频率32.Fourier transformation 傅立叶变化33.Decomposition 分解34.grain gradient 纹理梯度35.allometric model 异速生长模型36.fresh weight 鲜重37.Dry weight 干重38.Multicollinearity 多重共线性39.Overfitting 过度拟合40.successional vegetation classification 次生林分类41.classifier 分类器42.supervised classification监督分类43.unsupervised classification 非监督分类44.fuzzy classifier method 迷糊分类法45.maximum likelihood classification 最大似然法分类46.minimum distance classification 最小距离法分类47.Bayesian classification 贝叶斯分类48.Image analysis 图像分析49.feature extraction 特征提取50.feature analysis 特征分析51.pattern recognition 模式识别52.texture analysis 纹理分析53.ratio enhancement 比例增强54.edge detection 边缘检测55.image enhancement 影像增强56.reference data 参考数据57.auxiliary data 辅助数据58.principal component transformation 主成分变化59.histogram equalization 直方图均衡化60.image segmentation 图像分割61.geometric correction 几何校正62.geometric registration of imagery 几何配准63.radiometric correction 辐射校正64.atmospheric correction 大气校正65.synthetic aperture radar SAR 合成孔径雷达66.digital surface model, DSM 数字高程模型67.neighborhood method 邻近法68.least squares correlation 最小二乘相关69.illuminance of ground 地面照度70.geometric distortion 几何畸变71.mosaic 镶嵌72.pixel 像元73.quackgrass meadow 冰草草甸74.quagmire 沼泽地75.quantitative analysis 定量分析76.quantitative interpretation 定量判读77.radar echo 雷达回波78.radar image 雷达图像79.radar image texture 雷达图像纹理80.radiation 辐射81.rain intensity 降雨强度82.random distribution 随机分布83.random error 随机误差84.random sampling 随机抽样85.random variable 随机变量86.rare species 稀有种87.ratio method 比值法88.reafforestation 再造林89.reconnaissance survey 普查90.age structure 年龄结构91.recreation 休养92.afforestation 造林；植林93.recovery 再生94.abandoned land 弃耕地95.absorption 吸收〔作用〕96.climatic factor 气候因子97.reflected image 反射影像98.reforestation 森林更新99.regeneration cutting 更新伐100.regional remote sensing 区域遥感101.relative error 相对误差102.reliability 可靠性103.reversible process 可逆过程104.savanna forest 稀瘦原林105.heterogeneity 土壤差异性106.spectral resolution 光谱分辨率107.areal differentiation 地域分异108.substantial or systematic reproduction 实质性的或系统的繁殖109.initiated 开始110.converted 转变111.successional stages 演替系列112.uncertainties 不确定性113.soil fertility 土壤肥力nd-use history 土地利用历史115.vegetation age 植被年龄116.spatial distribution 空间分布117.field measurements 样地测量118.characteristics 特征119.Saplings 树苗120.primary data 原始数据nd cover 土地覆盖122. training sample 训练样本123.spectral signature 光谱特征124.spatial information 空间信息125.texture metrics 纹理度量126.texture measure 纹理测量127.data fusion 数据融合128.sensor 传感器129.multispectral data 多光谱数据130.panchromatic data 全色数据131.radar data 雷达数据132.classification algorithms 分类算法133.parametric 参数134.classification tree analysis 分类树135.K-nearest neighbor K近邻法136.Artifice alneural network (ANN) 神经网络137.per-pixel-based 基于像元的138.environmental features 环境要素139.preprocessing 预处理140.polarization 极化141.resampled 重采样142.image-to-image registration 影像到影像配准143.vegetation types 植被类型144.intensity-hue-saturation 亮度色度饱和度145.Brovey transform Brovey 变换146.Evaluated 评价147.error matrix 混淆矩阵nd use/cover classifation 土地利用/覆盖分类149.Misclassification 误分150.Classification accuracy 分类精度151.producer’s accuracy 生产者精度er’s accuracy 用户精度153.Optical multispectral image 光学多光谱影像154.optical sensor 光学传感器155.fusion techniques 融合技术156.uncertainty analysis 不确定性分析157.data saturation 数据饱和158.Parametric vs nonparametric algorithms 参数非参数算法159.global change 全球变化160.process model–based 基于模型的过程161.empirical model–based 基于经验的模型162.biomass expansion/conversion factor 生物量扩展/转换因子163.hyperspectral sensor 多光谱传感器164.radar data 雷达数据165.belowground biomass 地下生物量166.aboveground biomass 地上生物量167.GIS-based 基于GIS的168.ecosystem models 生态模型169.photosynthesis 光合作用170.anthropogenic effects 人为影响171.homogeneous stands 均一的立地条件172.empirical regression models 经验回归模型173.variables 变量174.subcompartment 小斑175.DBH 胸径176.Spectral features 光谱特征177.Spatial features 空间特征178.Subpixel features 亚像元特征179.Active sensor 主动传感器180.Lidar data 雷达数据181.vegetation indices 植被指数182.biophysical conditions 生物物理条件183.soil fertilities 土壤特征184.near-infrared 近红外185.extracting textures 纹理提取186.mean 均值187.variance 方差188.homogeneity 同质性189.contrast 对比度190.entropy 信息熵191.mature forest 成熟林192.secondary forest 次生林193.nonphotosynthetic vegetation 非光合作用植被194.shade fraction 阴影分量195.soil fraction 土壤分量196.biomass density 生物量密度197.vegetation characteristics 植被特征198.species composition 树种组成199.growth phase 生长期200.spectral signatures 光谱信息201.moist tropical 热带雨林202.primary data 原始数据203.unstable 不稳定204.soil moisture 土壤水分205.horizontal vegetation structures 水平植被结构206.canopy cover 灌层覆盖度207.canopy height 灌层高度208.regression technique 回归技术209.interferometry technique 干涉技术210.terrain properties 地形要素211.backscattering coefficient 后向散射系数212.canopy elements 灌层要素213.backscattering values 散射值214.coherence of data 数据一致性215.the total coherence of a forest 森林的一致性216.forest transmissivity 森林透射率rge scale biomass 大区域生物量218.Polarization Coherence Tomography 极化相干断层扫描219.filtering methods 滤波方法220.outliers 异常值221.stereo viewing 立体视觉ser return signal 激光反馈信号223.characterizing horizontal 水平特征224.characterizing vertical 垂直特征225.canopy structure 灌层结构226.biomass prediction 生物量预测227.height information 树高信息228.hypothetical example 假设样本229.mean height 平均树高230.univariate model 单变量模型231.metric 度量标准232.biomass accumulation 累计生物量233.categorical variables 绝对变量234.different source data 不同源数据235.DEM data DEM数据236.optimal variables 最佳变量237.expert knowledge 专家知识238.strong correlations 强相关239.weak correlations 弱相关240.stepwise regression analysis 逐步回归分析241.independent variables 独立变量242.Parametric algorithms 参数算法243.nonparametric algorithms 非参数算法244.linear regression models 线性回归模型245.nonlinearly related 非线性相关246.power models 指数模型247.nonlinear models 非线性模型248.random forest 随即森林249.support vector machine (SVM) 支持向量机250.Maximum Entropy 最大熵251.Simulation 仿真252.co-simulation 协同仿真253.normal distribution 正态分布254.spatial configuration 空间结构255.randomly setting 随机设置256.pixel estimation 像素估计257.sample variance 样本方差258.national forest inventory sample plot data 国家森林库存样地数据259.natural deciduous forests 自然落叶森林260.linear relationships 线性关系261.approximation 近似法262.mathematical functions 数学函数263.black-box model 黑箱模型264.iterating training 迭代训练265.root node 根节点266.internal nodes, 内部节点267.recursive partitioning algorithm 逐步分割算法268.stratified 分层269.terminal node 终端节点270.regression tree theory 回归树理论271.split 分割272.statistical learning algorithm 统计学习算法273.high-dimensional feature space 高维特征空间274.kernel 卷积核275.empirical averages 经验平均值276.subsections 分段277.Accurately estimating 精度评价278.relative errors 相对模糊279.global scales 全球尺度280.root mean square error (RMSE) 均方根误差281.correlation coefficient 相关系数282.systematic sampling 系统抽样283.data collection 数据收集284.subset 子集285.mapping forest biomass /carbon 生物量/碳储量制图286.sequestration 隔离287.forest management and planning 森林管理和规划288.allometric models 异速生长的模型289.representativeness 代表性290.lidar data 激光雷达数据291.vegetation structure gradient 植被结构梯度292.randomly perturbing 随机扰动293.north coordinates 北坐标294.coarser spatial resolution 粗分辨率295.grouping errors 分组误差296.Medium spatial resolution 中分辨影像297.population parameters 人口参数298.Mixed pixels 混合像元299.Mismatch 误差300.high spatial resolution images 高分辨率影像。

Land Cover Mapping in Support of LAI and FAPAR Retrievals from EOS-MODIS and MISR:Classification Methods and Sensitivities to ErrorsA.Lotsch,Y.Tian,M.A.Friedl,and R.B.MyneniDepartment of Geography,Boston University675Commonwealth Avenue,Boston,MA02215Corresponding author Mark Friedl;E-mail:friedl@Manuscript submitted for publication in:International Journal of Remote SensingDecember23,2000AbstractLand cover maps are widely used to parameterize the biophysical proper-ties of plant canopies in models that describe terrestrial biogeochemical pro-cesses.In this paper,we describe the use of supervised classification algo-rithms to generate land cover maps that characterize the vegetation types re-quired for LAI and FAPAR retrievals from MODIS and MISR.As part of this analysis,we examine the sensitivity of remote sensing-based retrievals of LAI and FAPAR to land cover information used to parameterize vegetation canopy radiative transfer models.Specifically,a decision tree classification algorithm is used to generate a land cover map of North America from A VHRR data with1km resolution using a6-biome classification scheme.To do this,a time series of normalized difference vegetation index data from the A VHRR is used in association with extensive site-based training data compiled using Landsat TM and ancillary map sources.Accuracy assessment of the map pro-duced via decision tree classification yields a cross-validated map accuracy of 73%.Results comparing LAI and FAPAR retrievals using maps from different sources show that disagreement in land cover labels generally do not translate into strong disagreement in LAI and FAPAR maps.Further,the main source of disagreement in LAI and FAPAR maps can be attributed to specific biome classes that are cahracterized by a continuum of fractional cover and canopy structure.1IntroductionVegetation and land cover play a key role in terrestrial biogeochemical processes, and changes in land cover induced by human activity have profound implications for climate,the functioning of ecosystems,and biogeochemicalfluxes at regional and global scales[Lean and Warilow1989;Dickinson and Henderson-Sellers 1988].As a consequence,a wide range of problems require reliable and accurate information on global land cover,and in particular,the distribution and properties of vegetation.With the launch of NASA's Terra platform,a new generation of satellite data is now available.For example,the Moderate Resolution Imaging Spectroradiometer (MODIS)on-board Terra is providing substantially improved data for land cover mapping relative to the heritage data provided by the Advanced Very High Resolu-tion Radiometer(A VHRR)[Justice et al.1998].Further,the Multi-Angle Imaging Spectroradiometer(MISR)provides image data of the Earth's surface with nine view angles for each pixel that will be particularly useful for retrieving information regarding the structural properties of vegetation canopies such as the leaf area in-dex(LAI)and the fraction of absorbed photsynthetically active radiation(FAPAR) [Knyazikhin et al.1998].The primary objective of this paper is to present results from remote sensing-based vegetation mapping in support of the EOS MODIS LAI/FAPAR algorithm [Knyazikhin et al.1998;Knyazikhin et al.1998].This algorithm uses radiative transfer models to retrieve information about the biophysical characteristics of plants from reflected solar radiation.The parameterization of such radiative transfer mod-els,however,is dependent on the structural properties of the plant canopy.Within this framework,the MODIS LAI/FAPAR algorithm recognizes six structurally dis-tinct biomes.In support of this effort a decision tree classification algorithm is used to create a land cover map of North America using the6-biome classification scheme.The primary data source that was used to produce this map is a12month time series of A VHRR NDVI data from February of1995to January of1996.Re-sults from this exercise are compared with maps produced by cross-walking existing 1km maps of global land cover produced by the University of Maryland(UMD) [Hansen et al.2000]and Eros Data Center(EDC)[Loveland et al.2000].As part of this analysis we specifically examine the sensitivity of LAI and FAPAR retrievals to errors in land cover labels.2Background2.1Global Vegetation and Land Cover Mapping Approaches Because of the diversity of vegetation at global scales,accurate mapping and repre-sentation of terrestrial vegetation has been a challenge for many years.For example, Townshend et al.[1991]compared existing maps of global vegetation and showed that estimates of vegetation distribution from common sources varied considerably. While such databases have obvious limitations,until recently they represented the state of the science for driving large scale process models.There is wide consensus that remotely sensed data can provide an accurate and repeatable means of land cover mapping and monitoring,especially with respect toareas with rapidly changing land use and land management activities[Running et al. 1994;Townshend et al.1991].In particular,remote sensing-based approaches are able to exploit distinct spectral properties from different land cover types and tem-poral information related to phenological dynamics in vegetation[Loveland et al. 1991;Justice et al.1985].Prior to the launch of Terra,most research on global land cover mapping has used data collected by the Advanced Very High Resolution Radiometer(A VHRR)instrument on board the National Oceanic and Atmospheric Administration(NOAA)series of satellites[Justice et al.1985;Loveland et al. 1995;Running et al.1994].Although recent work has provided promising results,it must be noted that the utility of A VHRR data for land cover applications is limited in several regards in-cluding the high level of atmospheric noise,lack of onboard calibration,and limited spectral information[Zhu and Yang1996;Cihlar et al.1997;Moody and Strahler 1994].The MODIS instrument is expected to overcome many of these limitations [Strahler et al.1996;Friedl et al.2000].Specifically,MODIS provides superior spectral and spatial resolution,atmospheric correction,and calibration relative to A VHRR data Running et al.[1994,Barnes et al.[1998,Justice et al.[1998].2.2Remote Sensing of LAI and FAPARThe relationship between NDVI and LAI and FAPAR has been well established through both theoretical and empirical studies.However,the utility of this relation-ship depends on the sensitivity of these variables to canopy characteristics[Myneni et al.1997].While FAPAR exhibits a positive linear relationship with increasingNDVI,LAI is non-linearly related to NDVI,saturating at LAIs of3-6,depending on the vegetation type.In order to estimate LAI and FAPAR from remotely sensed data,canopy structural types must be defined that exhibit different NDVI-LAI or FAPAR relations from one another.Therefore many classification schemes,which are based on ecological,botanical,or functional metrics are not necessarily suitable for LAI and FAPAR retrieval.Most LAI and FAPAR retrieval algorithms are based on inversion of radiative transfer models,which simulate radiation absorption and scattering in vegetation canopies.A review of such models can be found in Myneni et al.[1995].The algorithm being used to retrieve LAI and FAPAR from MODIS and MISR data is based on six distinct plant structural types(biomes)defined by Myneni et al. [1997].The MODIS/MISR LAI and FAPAR retrieval algorithm therefore relies on a database describing the global distribution of these biomes to invoke different radiative transfer models.The definitions and properties of the six biomes as they relate to radiative transfer are presented in table2.2.3Tree-Based Classification AlgorithmsA variety of different techniques are currently used to classify remotely sensed data for land cover and vegetation mapping applications.Traditionally,land cover map-ping approaches have used either parametric supervised classification algorithms or unsupervised classification algorithms.These latter algorithms use clustering techniques to identify spectrally distinct groups of data[Schoewengerdt1997],and have been widely used with high resolution imagery such as Landsat or SPOT.Global land cover classification efforts,however,have mostly employed coarse resolution data from the A VHRR[Loveland et al.2000].These efforts have used un-supervised clustering in conjunction with ancillary data and manual labeling[Love-land et al.1991],maximum likelihood classification[DeFries and Townshend1994], or hierarchical classification logic based on structural and biophysical parameters[Run-ning et al.1995].More recent approaches include applications of neural networks including fuzzy neural networks[Carpenter et al.1992;Gopal and Woodcock1996].Recently,decision tree algorithms have been used to classify global datasets with promising results[Friedl and Brodley1997;Friedl et al.1999;DeFries et al. 2000;Hansen et al.2000].Decision trees are computationally efficient andflexible, and also have an intuitive simplicity[Safavian and Landgrebe1991].They there-fore have substantial advantages in remote sensing applications[Friedl and Brodley 1997].Decision trees recursively partition the data set to be classified into increasingly homogeneous subsets based on a set of splitting rules.The tree has a root,which represents the entire data set,a set of internal nodes(splits),and a set of terminal nodes at the bottom of the tree(leaves).Every node in the tree(except the terminal nodes)has one parent node and two(or more)descendant nodes.The nodes rep-resent subsets of the data set,while the terminal nodes represent the predictions of the tree.Thus,each observation is labeled according to the majority class of the leaf in which it falls[Breiman et al.1984].A classic example is the classification and regression tree(CART)model described by[Breiman et al.1984].Tree-based methods are therefore categorized as supervised techniques and a training data setis required from which the decision tree is estimated.3MethodologyThe analysis presented below involves four main components.Section3.1describes the data along with the decision tree classification algorithm that was used to gener-ate land cover maps using the6-biome classification scheme.Section3.2explains the steps that were taken to translate(cross-walk)existing classification products into biomes throughout the analysis,and section3.3discusses how the maps were compared.Finally,section3.4describes how the LAI and FAPAR retrievals were performed and assessed.3.1Data and Classification MethodsThe classification analyses presented below were based on a12month A VHRR NDVI time series.The data set was composed of monthly composited NDVI data covering the time period between February1995and January1996.The training data used for this analysis were extracted from a database of global land cover train-ing sites that was compiled by the MODIS Land Cover and Land-Cover Change group at BU[Muchoney et al.1999;Friedl et al.2000;Strahler et al.1996].This database contained approximately1000sites in North America and has undergone several iterations of quality control.Each site in the database possessed an areal extent ranging between2and100,a label assignment defined by the IGBP classification scheme[Loveland and Belward1997],and where possible,a set ofbiophysical parameters that describe the ecological and biophysical conditions of the site[Muchoney et al.1999].The label and attribute assignments were per-formed using recent TM imagery along with ancillary data sources such as existing paper or digital maps,literature sources,aerial imagery,and ground information provided by collaborating science teams.Each training site was registered to co-ordinates in the Universal Transverse Mercator Projection(UTM),converted to a raster image format with a30m resolution,aggregated to a1km resolution,and reprojected to the Integerized Sinosoidal Grid(ISG)Projection used for MODIS products.A key step in developing the training site database was to remove statistical outliers to avoid unwanted confusion in the classification algorithm.To do this,a two step generalized gap test for multivariate outlier detection was performed [Rohlf1975].In thefirst step,the largest outlier in each training site was removed from the training data with the intent of increasing the homogeneity in each site.In the second step,sites were identified as outliers within each class to decrease within-class heterogeneity.Examples of representative outliers are shown infigure 1.A total of35sites(768pixels)were removed from the training data based on this analysis.Classification performance was assessed using cross-validation procedures.Specif-ically,the site data was randomly split into5mutually exclusive subsets,where80%of the data were used for training and20%were used as an independent test sam-ple.For each80/20split a decision tree was estimated using the training sample, and its performance was evaluated on the independent test sample.In this way,theinformation contained in the test sample was previously unseen(independent)and not used to build the tree.This procedure was repeated for each80/20train and test split,and the classification accuracies presented herein are reported as averages across thefive cross-validation runs.Since the training sites were defined such that the within-site homogeneity was maximized,substantial spatial autocorrelation was present in the A VHRR data within sites.Spatial autocorrelation can significantly impact accuracy assessment measures[Congalton1988].Conceptually,the prediction of a pixel's class value becomes“easier”for the classification algorithm based on prior information from adjacent pixels[Friedl et al.2000].Therefore,to ensure truly independent train and test splits,the splitting procedure stratified the data on the basis of entire sites(as opposed to randomly splitting the pooled data irrespective of the sites from which the pixels were derived).To produce thefinal map,all the training data were pooled and afinal tree was built based on the entire training data set.This tree was then used to classify the NDVI image dataset(i.e.,to generate the biome map of North America).Because of data quality issues related to radiometric quality,errors in geometric rectification,cloud screening,and labeling errors by analysts,the site database was carefully screened prior to using it for analyses.This was accomplished in three steps.First,missing values in the A VHRR NDVI data(data dropouts),particu-larly in northern latitudes,werefilled using temporal smoothing and interpolation routines.Second,due to misregistration of some of the TM scenes,not all sites could be used in the analysis.Out of the approximately1000sites only665wereused.This issue was particularly pronounced at high latitudes.As a result,land areas in the northern part of the continent were undersampled.To compensate,32 new training sites were added based on areas of agreement between the UMD and EDC maps.This approach was justified based on the assumption that confidence in class labels is high where two independently generated maps agree.The sites were chosen randomly across the undersampled region with sufficient distance between each other to remove the effect of spatial autocorrelation.Finally,to compensate for oversampled classes in the site database,the training data were resampled to re-flect the expected proportions of land cover based on the proportions of each class in the UMD and EDC maps for North America.In cases where the number of train-ing pixels available in a class was below the threshold required to characterize the properties of the class,all the pixels were kept.In cases where the class size was too large,a random sample proportional to the estimated frequency of the class on the ground was generated and used for further analysis.3.2Cross-Walking from IGBP Classes to BiomesTranslation of different classification schemes often can not be done in an unam-biguous fashion and may introduce unwanted errors and inaccuracies.A critical step for the work presented here was to translate the training data from the Inter-national Geosphere-Biosphere Program(IGBP)classification scheme into the6-biome classification scheme.In particular,direct translation of the17IGBP classes into the six biome classes is not possible for IGBP classes5,6,8,12,14(mixed forest,closed shrublands,woody savanna,croplands and croplands mosaic,respec-tively).To resolve these ambiguities,the seasonal land cover region characterization (SLCR)database[Loveland et al.2000]was used as an ancillary data source.This database possesses significantly more classes than the IGBP scheme,and therefore much narrower class definitions.Specifically,the SLCR database defines approx-imately200classes for each of thefive major continents globally(205classes for North America,963globally).The narrow definition of the SLCR classes allows their aggregation into classification systems with broader class definitions(e.g.,the IGBP scheme)[Loveland et al.2000].Look up tables(LUT)to aggregate SLCR classes into various classification schemes(e.g.,Olson,Simple Biosphere Model, etc.)are provided by EDC and were used as a guideline for translating SLCRs to the6biomes.For this work,a LUT was used to assign a biome label to each training site for those cases where the training site possessed an ambiguous IGBP label(classes 5,6,8,12,14,16;Table1).The relabeled training sites were then used as input to the classification process as described above(pre-classification aggregation).To accomplish this task,a SLCR label for each training site was obtained by overlaying training sites with the SLCR map.The most common SLCR within the training site polygon was used as the assigned SLCR label.The SLCR and IGBP labels were then compared and examined for agreement.In40cases(sites)the training site label and the corresponding SLCR label could not be reconciled with each other. These cases were therefore removed from further analysis.3.3Comparison of Supervised Classification Results with Exist-ing MapsIn the second part of the analysis a quantitative analysis of the6-biome map pro-duced by cross-walking both the UMD and EDC global land cover maps was per-formed.This served two purposes.First,it provided a baseline comparison regard-ing the quality of the map produced with the decision tree classification algorithm relative to published map datasets.Second,it highlighted the strengths and weak-nesses of each map and provided a basis for selecting a6-biome data base for use in global retrieval of LAI and FAPAR using the MODIS LAI/FAPAR algorithm.Be-low we provide brief descriptions of these map products.For detailed descriptions of the classification algorithms used to generate the UMD and EDC datasets,see Hansen et al.[2000],Loveland et al.[1995]and Loveland et al.[2000].The EDC classification follows the IGBP scheme and includes17classes of landcover and vegetation.The classification scheme used by UMD basically fol-lows the IGBP classification logic.However,three IGBP classes are not included in the UMD scheme:snow and ice,permanent wetland,and cropland mosaic.There-fore these three classes were excluded from further analysis.Both maps were cre-ated using A VHRR data from1992and1993and have a spatial resolution of1 km.The three map products(i.e.,the UMD-based map,the EDC-based map,and the supervised classification map)were assessed using the training site data compiled by the Land Use and Land Cover Change project at Boston University.Specifi-cally,the database of training sites compiled at BU provides extensive site-based land cover data for North America and can therefore be used as an independent data set to evaluate the EDC and UMD-based maps.For reasons described be-low,slightly different methods were used to do this.Specifically,the BU-biome map was assessed using cross-validation statistics as described above.To assess the UMD and EDC-based maps,cross-validation is not required and the analysis was performed using entire sites,rather than pixels.This approach was felt to provide more conservative estimates.Classification accuracy in this paper is assessed using confusion matrices.These matrices document errors of omission and commission by cross-tabulating labels predicted by the classification algorithm with labels obtained from ground truth mapping[Congalton1991].In addition,the kappa coefficient()[Cohen1960]is used,which provides a correction for the proportion of chance agreement between reference and test data[Rosenfield and Fitzpatrick-Lins1986].In this paper denotes the probability that a pixel classified as class in the map is labeled as class in the reference data,and denotes the probability that a pixel labeled as class in the reference data is classified as class in the map.Errors of commission and errors of omission are defined as1-and1-,respectively.The overall proportion of correctly classified pixels is denoted as.3.4Sensitivity to Errors of LAI and FAPAR retrievalsThe third part of this analysis focuses on comparisons of LAI and FAPAR retrievals using the same A VHRR data,but based on the land cover maps by BU,UMD andEDC.As a baseline,the frequency distribution of LAI and FAPAR are compared,as well as the mean and standard deviation of LAI and FAPAR as a function of biome class.Also,the area of agreement for LAI and FAPAR between the three maps is benchmarked against the ratio of agreement in the underlying landcover maps.The magnitude of difference in LAI and FAPAR between two landcover maps was also examined.Differences in LAI and FAPAR between each pair of maps were categorized into3classes(LAI=0,0LAI1,and LAI1and FAPAR=0,0FAPAR0.1,and FAPAR0.1).Differences smaller than0.1were set equal to0for both LAI and FAPAR.Confusion tables were then created for each of these3classes.This provides insight regarding how confusion in two biome types affects the retrievals of LAI and FAPAR.4Results4.1Decision Tree Classification PerformanceTable3presents cross-validated classification results for the supervised classifica-tion of6-biome classes in North America in the form of a confusion matrix.Overall, classification accuracies()are quite good(73%),but are variable across classes. Below we summarize the results for each class.Grasses and Cereal Crops(Biome1):Biome1generally exhibits high errors of omission with respect to the forest classes,shrublands and savannas,while omission errors for broadleaf crops are not as pronounced.Confusion is highest for the forest classes,which are structurally distinct from biome1.The misclassification rate forbiome4(savannas)of6%was comparable to the misclassification rate for broadleaf forests(5%).This result likely relates to the spectral properties of savannas,which possess up to80%grass understory.Shrubs and needleleaf forest exhibit the highest commission errors for biome1.Shrubs contributed14%to the total commission error(35%),and needleleaf forests contributed7%(Table3).Shrubs(Biome2):Background reflectance properties are a key factor influenc-ing the variability in remotely sensed data over shrublands.Whereas shrublands in the western part of the continent have bright backgrounds,shrublands in the sub-arctic region are more similar to savannas in terms of their NDVI.This confusion is evident in Table3,where13%of the grasses/cereal crops pixels and24%of the savanna pixels contribute to a total omission error of40%.The commission error for shrubs is generally smaller than the omission error,i.e.classes1and3-6are less often classified as shrubs,than shrubs are classified as one of the other classes.Broadleaf Crops(Biome3):For broadleaf crops,Table3shows that the highest omission errors were associated with savannas,whereas the highest commission errors were generally contributed by biome1(column1in table3).The latter had an important influence on the proportion of the two cropland classes in the biome maps.Again,confusion between forests and broadleaf crops is more severe in terms of misclassification costs relative to confusion with biome1.Savannas(Biome4):The most serious source of confusion for this class arose from confusion between savannas,grasslands and cereal crops,and forests.This is clearly related to the properties of this class,which is a mixture of both grasses and trees.Also,savannas represent a small portion of the training data and weresomewhat penalized by the classification algorithm.Broadleaf Forests(Biome5):Broadleaf forests had the highest and. The largest errors in this regard arose from confusion between broadleaf forests, needleleaf forests,and grasses and cereal crops.Confusion with the latter class probably arose because of the similar temporal pattern in NDVI for each of these classes.Misclassification of biome5as needleleaf forests is probably explained by naturally occuring mixtures of both classes.Needleleaf Forests(Biome6):The most severe sources of misclassification for this class was the classification of needleleaf forests as grasses,which totals6% (table3).This problem could not be entirely resolved and is evident in thefinal biome map.Finally,the non-vegetated class exhibited small but significant confusion with all6biome classes.In particular,biome1was frequently assigned to the non-vegetated class and vice versa.This is not surprising since many agriculturalfields are non-vegetated for a number of months a year.Some misclassification of class7 as shrubs was also observed.Note that shrubs are defined by low vegetation density and bright backgrounds,which is similar to bare ground.4.2Map ComparisonsIn this section,an accuracy assessment of the UMD and EDC-based6-biome maps is presented,using the training sites from the analysis above as reference data.The areal distribution of land cover classes from each of these maps is then compared with each other.Error matrices are used to identify confusion among particularclasses in both classification schemes.4.2.1Accuracy Coefficients for the UMD and EDC MapsTo perform a site-based accuracy assessment for the biome maps derived by cross-walking the UMD and EDC maps,the BU training sites were overlayed with each map.However,because some sites included mixtures of classes in the EDC and UMD-based maps,not all of the sites were used to estimate the error matrices. Specifically,the most frequent class in each map in each polygon was assigned to each site,and only those sites that were90%covered by one biome were used.This threshold was chosen because it provided high confidence in the class label,while maintaining a sufficiently large sample in each class.Also,sites that were detected as outliers(Section3.1)were not used in the error matrix.Unfortunately,this re-duced the number of available sites for the analysis.At the same time,because the sample size is still relatively large(306)and the procedures described above are designed to be conservative,the results should be relatively reliable.Results from the analysis of the UMD-based biome scheme are shown in Table 4.Overall accuracy and are83%and0.75,respectively.High is shown for biome3and5.Biome3and the non-vegetated class also exhibit a of100%.Table5shows the analysis for the EDC map in the biome scheme.The overall accuracy was84%and was0.76.Shrubs(biome2)and broadleaf crops(biome3) had a of100%.Broadleaf forests were classified with96%accuracy,whereas needleleaf forests exhibited an accuracy of only59%().4.3LAI and FAPAR RetrievalsThe mean and standard deviation for LAI and FAPAR for each class and for each map are shown in Table6.With the exception of biome1(grasses and cereal crops) in the BU map,the mean and standard deviation for the three maps agree very well for both LAI and FAPAR,and are in accordance with published and theoretical values[Myneni et al.1997].Table7summarizes the overall agreement in LAI,FAPAR and land cover.To generate these estimates pixels with differences smaller than0.1were considered to have the same LAI and FAPAR,which slightly inflates the rate of agreement. Nonetheless,the comparison shows that agreement in LAI and FAPAR are roughly 20-25and40-45(respectively)greater than for land cover.This result suggests that the LAI and FAPAR algorithm is relatively robust to uncertainty in land cover.Visual inspection of each of the maps and the results presented in Sections4.1 and4.2suggest that at continental scales each of the maps appears to be capturing the distribution of each biome.However,this does not necessarily mean that there is agreement in the spatial distribution of LAI and FAPAR across continental scales.This question is addressed in Tables8and9,which stratify interclass confusion by the magnitude of LAI and FAPAR differences.In Table8,the top confusion matrix shows the percentage of pixels that agree in LAI(I0.1),the middle matrix presents the percentage for which0LAI1and the bottom matrix presents the percentage of pixels whose difference was greater than0.1. Similarly,Table9presents differences in FAPAR categorized according to FAPAR =0,0FAPAR0.1,and FAPAR0.1.The tables show comparisons for。

AbstractWith the rapid development of remote sensing technology, a large amount of remote sensing data has been provided for a variety of remote sensing applications. While due to the lack of effective quality evaluation system and methods of remote sensing data in specific application areas, there is great blindness in the acquisition, processing and application of remote sensing data. This lack of awareness of data availability will no doubt affect research in image processing and analysis algorithms, and the evaluation of various processing and analysis of image will be lack of effectiveness. All of these will hinder the expansion of the amplitude and deepening of the level of remote sensing applications.This paper proposes two classification-oriented image quality assessment methods on the basis of analysis of the quality of image classification. The first image quality assessment method is based on image quality factors. For the first method, this paper first analyzes the impact of image quality factors on image quality, and then explores the connection between image quality factors and image classification accuracy; the second image quality assessment method is based on the distribution of image features. For the first method, this paper first builds the image quality model which reflects the relationship between the distribution of image features and image classification accuracy, and then evaluates the image quality using the image quality evaluation model. Since the EM algorithm can easily get trapped in local optimal solution, which may deviate far from the true value, this paper proposes a MCEM (Mean-Constrained Expectation Maximization) algorithm. Compared with the EM algorithm, both theory and experiments show that MCEM algorithm can effectively improve the accuracy of parameter estimation.The experimental results using both the experimental simulation data and remote sensing data, verify the feasibility of the first method proposed in this paper. The experimental results using remote sensing data verify the validation of the second method. Experimental results show that the proposed second method of image quality evaluation, which is based on the distribution of image features can effectively estimate the accuracy of image classification.Keyword: classification-oriented image quality assessment classification accuracy parameter estimation目录摘要 (I)ABSTRACT (II)1 绪论1.1引言 (1)1.2国内外研究现状 (3)1.3研究方法及技术方案 (5)1.4论文结构及主要内容 (6)2 面向图像分类的图像质量评价模型2.1图像分类精度评价指标——K APPA系数的分析 (7)2.2图像质量评价模型的建立 (9)2.3本章小节 (12)3 基于质量要素进行图像质量评价3.1通用图像质量方程 (13)3.2图像质量要素分析与度量 (16)3.3图像质量要素与图像分类精度的关系研究 (18)3.4本章小结 (28)4 基于特征分布的图像质量评价4.1概率密度估计算法 (29)4.2基于特征分布的图像质量评价 (43)4.3基于特征分布的图像质量评价方法性能评价 (46)4.4本章小结 (47)5 总结与展望5.1本文总结 (49)5.2未来工作与展望 (50)致谢 (51)参考文献 (52)1 绪论1.1 引言遥感技术由于其获取数据的范围广、实时性好以及综合性强等优点，已成为空间探测的重要手段。

remote sensing letters 国际标准简称Remote Sensing Letters（RSL）是Remote Sensing领域的国际学术期刊，其国际标准简称是“RSL”。

Remote Sensing Letters（RSL）致力于推动遥感技术的发展和应用，涵盖了遥感技术、地球科学、环境科学、大气科学、地理信息系统等多个领域。

该期刊主要发表遥感领域的最新研究成果，包括遥感影像获取、处理、分析、解译和地物分类等方面的论文，以及遥感技术在各领域的应用实践。

同时，RSL还关注遥感技术的发展趋势和前沿动态，为遥感领域的学者和工程师提供了一个交流和展示研究成果的平台。

遥感图像分类的精度评价（kappa统计值与分类精度的对应关系）遥感图像分类的精度评价精度评价是指⽐较实地数据与分类结果，以确定分类过程的准确程度。

分类结果精度评价是进⾏⼟地覆被/利⽤遥感监测中重要的⼀步，也是分类结果是否可信的⼀种度量。

最常⽤的精度评价⽅法是误差矩阵或混淆矩阵（Error Matrix ）⽅法（Congalton ，1991；Richards ，1996；Stehman ，1997），从误差矩阵可以计算出各种精度统计值，如总体正确率、使⽤者正确率、⽣产者正确率（Story 等，1986），Kappa 系数等。

误差矩阵是⼀个n ×n 矩阵（n 为分类数），⽤来简单⽐较参照点和分类点。

⼀般矩阵的⾏代表分类点，列代表参照点，对⾓线部分指某类型与验证类型完全⼀致的样点个数，对⾓线为经验证后正确的样点个数（Stehman ，1997）。

对分类图像的每⼀个像素进⾏检测是不现实的，需要选择⼀组参照像素，参照像素必须随机选择。

Kappa 分析是评价分类精度的多元统计⽅法，对Kappa 的估计称为KHAT 统计，Kappa 系数代表被评价分类⽐完全随机分类产⽣错误减少的⽐例，计算公式如下：2N.(.)K=(.)rii i i i i i x x x N x x ++∧++--∑∑∑式中 K ∧是Kappa 系数，r 是误差矩阵的⾏数，x ii 是i ⾏i 列（主对⾓线）上的值，x i +和x +i 分别是第i ⾏的和与第i 列的和，N 是样点总数。

Kappa 系数的最低允许判别精度0.7（Lucas 等，1994）表1 kappa 统计值与分类精度对应关系 (Landis and Koch 1977)Table1 classification quality associated to a Kappa statistics value1. Congalton, R. G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing Environ., 1991, 37: 35-46.2. Richards, J. A. Classifier performance and map accuracy. Remote Sensing Environ. 1996, 57:161-166.3.Stehman, S. V. Selecting and interpreting measures of thematic classification accuracy.Remote Sensing Environ., 1997, 62: 77-89.4.Story, M. and Congalton, R. G. Accuracy assessment: a user’s perspective. PhotogrammetricEngineering & Remote Sensing, 1986, 48(1): 131-137.5.Lucas, I. F. J., Frans, J. M. Accuracy assessment of satellite derived land-cover data: a review.Photogrammetric Engineering & Remote Sensing, 1994, 60(4): 410-432.。

遥感图像变换的实习总结与反思英文回答：Remote Sensing Image Transformation Internship Summary and Reflections.During my internship in remote sensing image transformation, I gained valuable experience in the field of image processing and analysis. I was involved in various projects that focused on developing and applying image transformation techniques to extract meaningful information from remote sensing data.One of the major projects I worked on was the development of a new algorithm for image classification. This algorithm used a combination of supervised and unsupervised learning techniques to achieve high accuracy in classifying different land cover types. I also worked on a project that involved the use of image segmentation to extract objects of interest from remote sensing images.These objects were then used for further analysis, such as object-based image classification.In addition to my project work, I also had the opportunity to learn about other aspects of remote sensing, such as data acquisition, pre-processing, and post-processing. I gained a good understanding of the entire remote sensing workflow, from data collection to the generation of final products.Overall, my internship experience was very rewarding. I learned a great deal about remote sensing image transformation and its applications in various fields. I also developed valuable skills in image processing, programming, and data analysis.中文回答：遥感图像变换实习总结与反思。