基于SVM的高光谱遥感图像亚像元定位

提出了基于支持向量机（support vector machine，SVM）的高光谱遥感图像亚像元定位方法。全变分（total variation，TV）模型是经典的保边缘平滑滤波器，本文将其引入作为预处理，来提高混合像元分解及亚像元定位的精度；本文方法在训练和检验样本的构建过程中，依据空间相关性理论，同时考虑了中心像元及其邻近像元丰度值对亚像元类别归属的影响；在监督分类训练和检验过程中，通过剔除纯净像元来缩减样本数量，在保证算法准确性的同时提高了效率。对真实高光谱遥感数据进行了实验，主观评价和定量分析验证了本文方法的有效性。

Sub-pixel Mapping Based on SVM of Hyperspectral Remotely Sensed Imagery

In this paper, an image sub-pixel mapping algorithm based on support vector machine (SVM)has been presented for hyper spectral imagery. Since the total variation(TV) model is classic Edge-preserving smoothing filter, the authors introduce this model as a presmoothing to improve accuracies of spectral unmixing and sub-pixel mapping. Also, according to the spatial correlation theory, our algorithm not only considers the impact of the abundance for the current pixel on sub-pixel classification, but also takes the effect of adjacent pixels into account. In addition, to improve the efficiency of our algorithm, we propose to decrease the number of samples by eliminating pure pixels during the training and testing procedure in supervised classification. Experimental results on real-world hyper spectral remote sensing dataset show the validity of our algorithm on both visual inspection and quantitative analysis.