一种基于多特征组合的EnMap-BOX土地利用分类方法研究

为研究多特征组合在国产高分卫星影像土地利用分类中的应用效果,本文采用GF6-WFV多光谱影像数据,构建基于光谱波段、植被指数、纹理特征的多特征组合,并采用ReliefF算法进行特征优选获取信息量冗余较小的优选特征集合,结合EnMap-BOX工具包寻优改进的SVM算法中惩罚参数C和核函数系数g获取最优分类模型,对研究区进行分类。结果表明:1)特征选择能够较好地降低多特征集合的信息量冗余。2)基于特征选择的改进SVM算法模型能获取较高的土地利用分类精度,总体精度达到82.89%,Kappa系数达到0.78,可以为土地利用分类提供一种具有较高应用价值的方法。

基于EnMAP-Box的遥感图像分类研究

采用2007年6月云南省勐腊县TM遥感数据,利用EnMAP-box进行了支持向量机的图像分类研究,以网格搜索法寻找最优参数,在设定的范围内,求得了最优C和g参数,用此参数进行支持向量机的遥感图像土地覆盖分类。结果表明:SVM方法较最大似然分类方法具有较高的分类精度,特别是阔叶林和橡胶林的精度明显优于最大似然分类方法;对于面积较小的次要类型,2种分类方法的精度基本保持一致;SVM的总体精度相对于最大似然分类提高了11.9%。

Leaf chlorophyll content retrieval of wheat by simulated RapidEye, Sentinel-2 and EnMAP data

Leaf chlorophyll content(LCC)is an important physiological indicator of the actual health status of individual plants.An accurate estimation of LCC can therefore provide valuable information for precision field management.Red-edge information from hyperspectral data has been widely used to estimate crop LCC.However,after the advent of red-edge bands in satellite imagery,no systematic evaluation of the performance of satellite data has been conducted.Toward this end,we analyze herein the performance of winter wheat LCC retrieval of currant and forthcoming satellites(RapidEye,Sentinel-2 and EnMAP)and their new red-edge bands by using partial least squares regression(PLSR)and a vegetation-indexbased approach.These satellite spectral data were obtained by resampling ground-measured hyperspectral data under various field conditions and according to specific spectral response functions and spectral resolution.The results showed:1)This study confirmed that RapidEye,Sentinel-2 and EnMAP data are suitable for winter wheat LCC retrieval.For the PLSR approach,Sentinel-2 data provided more accurate estimates of LCC(R2=0.755,0.844,0.805 for 2002,2010,and 2002+2010)than do RapidEye data(R2=0.689,0.710,0.707 for 2002,2010,and 2002+2010)and EnMAP data(R2=0.735,0.867,0.771 for 2002,2010,and 2002+2010).For index-based approaches,the MERIS terrestrial chlorophyll index,which is a vegetation index with two red-edge bands,was the most sensitive and robust index for LCC for both the Sentinel-2 and EnMAP data(R2≥0.628),and the indices(NDRE1,SRRE1 and CIRE1)with a single red-edge band were the most sensitive and robust indices for the RapidEye data(R2≥0.420);2)According to the analysis of the effect of the wavelength and number of used red-edge spectral bands on LCC retrieval,the short-wavelength red-edge bands(from 699 to 734 nm)provided more accurate predictions when using the PLSR approach,whereas the long-wavelength red-edge bands(740 to 783 nm)gave more accurate predictions when using the vegetation indice(VI)approach.In addition,the prediction accuracy of RapidEye,Sentinel-2 and EnMAP data was improved gradually because of more number of red-edge bands and higher spectral resolution;VI regression models that contain a single or multiple red-edge bands provided more accurate predictions of LCC than those without red-edge bands,but for normalized difference vegetation index(NDVI)-,simple ratio(SR)-and chlorophyll index(CI)-like index,two red-edge bands index didn’t significantly improve the predictive accuracy of LCC than those indices with a single red-edge band.Although satellite data with higher spectral resolution and a greater number of red-edge bands marginally improve the accuracy of estimates of crop LCC,the level of this improvement remains insufficient because of higher spectral resolution,which results in a worse signal-to-noise ratio.The results of this study are helpful to accurately monitor LCC of winter wheat in large-area and provide some valuable advice for design of red-edge spectral bands of satellite sensor in future.

基于EnMAP卫星和深度神经网络的LAI遥感反演方法

区域叶面积指数(Leaf Area Index,LAI)定量反演是开展大尺度农作物长势监测和产量估算的重要基础。针对当前区域LAI遥感定量反演存在的反演精度不理想和模型稳定性弱等问题,提出了一种基于少量训练样本进行LAI高精度反演的深度神经网络(Small Simple Learning LAI-Net,SSLLAI-Net)。该网络由2个卷积层、1个池化层和3个全连接层构成,将光谱反射率数据作为网络输入端、输出端得到LAI反演值,且该网络模型可支持小样本数据量的训练。以德国阿尔卑斯山麓高光谱遥感卫星影像Environmental Mapping and Analysis Program(EnMAP)为数据源,以该区域的谷物、玉米、油菜、其他作物为研究对象,数值实验结果表明当各作物类别的训练样本量均为50时,基于SSLLAI-Net的LAI反演精度分别为0. 95、0. 99、0. 98、0. 90;且在添加噪声的情况下,各作物类别的LAI反演精度分别为0. 95、0. 98、0. 96、0. 89。综上,提出的基于深度神经网络的区域LAI遥感定量反演方法 SSLLAI-Net是鲁棒可靠的,且该模型能够支持稳定的小样本建模。

多特征组合的TM影像EnMap-Box土地利用分类

为研究多特征组合对提高遥感影像土地利用分类精度的作用,以云南省洱源县作为研究区域,利用EnMAP-Box软件对选取的多特征组合向量进行支持向量机(support vector machines,SVM)分类。本文选取了绿度植被指数、归一化建筑指数及基于灰度共生矩阵提取的纹理信息和最优波段组合等光谱特征构成分类多特征组合向量,通过EnMAP-Box软件寻优SVM最佳分类模型对多特征组合向量进行遥感影像土地利用分类。同时选择了云南省思茅区验证此法的适用性。结果表明,基于多特征组合的支持向量机分类法其总体分类精度为90.73%,分别比最大似然分类法高13%左右,比原始波段影像的分类精度高大约7%左右,另一验证区域精度结果表明此法具有一定适用性。