基于混合像元分解提取大豆种植面积的应用探讨

利用中低分辨率卫星影像进行大宗作物面积提取时,需要考虑混合像元产生的影响,以提高面积提取的精度.以吉林省梨树县大豆种植面积为例,选取线性光谱混合模型对TM影像进行分类并计算出大豆种植面积,将其结果与Quickbird影像解译结果对比分析,采用以数量精度为基础的精度评价方法,分类精度达到92%.同时,使用典型的最大似然法监督分类和自组织迭代法非监督分类提取大豆种植面积,分类精度分别为87%和84%.结果表明,混合像元分解方法与其他遥感定量提取方法相比,能够提高大豆种植面积提取的精度.

Application research on the method for extracting soybean covered areas based on the pixel unmixing

When estimating the yield of large area crops from the low and middle resolution images of the remote sensing satellites, the influence of the mixed pixel should be considered in order to improve the accuracy of the soybean covered areas extraction. This paper selected Lishu County of Jilin Province as a test area, applying the Linear Spectral Model (LSMM) to classify TM images and evaluate soybean covered areas. The result was compared with the interpretation result from Quickbird image. According to the quantity accuracy assessment method, the classification accuracy of soybean covered areas reached 92%. This paper also extracted soybean covered areas by the maximum likelihood supervised classification and the isodata unsupervised classification. The corresponding accuracies are 87% and 84%, respectively. Result shows that the pixel unmixing techniques can improve the classification accuracy of extracted soybean covered areas comparing with other quantification methods of remote sensing.