根据高光谱遥感图像数据维度高、空间相关性、特征非线性的特点,提出了一种基于深度学习的空-谱特征提取分类算法。首先在堆栈稀疏自动编码机中加入权重衰减项,再利用主成分分析方法对图像数据进行降维处理,然后根据主成分影像块内所有像元的第一主成分与中心像元间的差距对邻域信息进行排序、删除、重组和堆栈,最后将得到的空-谱信息输入到与SoftMax分类器相结合的堆栈稀疏自动编码机中进行分类。通过两组实验数据的对比,验证了所提分类算法可以提高高光谱图像的分类精度。

Hyperspectral remote sensing image data have characteristics of high dimension, spatial correlation, and feature nonlinearity, based on which a spatial-spectral feature extraction classification method based on deep learning is proposed herein. First, the weight decay is added to a stacked sparse auto-encoder. Next, the principal component analysis method is used to reduce the dimensionality of the image data. Then, neighborhood information is sorted, deleted, reorganized, and stacked according to the difference between the first principal component of all pixels in the principal component image block and the central pixel. Finally, the obtained spatial-spectral information is input into a stacked sparse auto-encoder combined with the SoftMax classifier for classification. The comparison of two sets of experimental data reveals that the proposed classification algorithm improves the classification accuracy of hyperspectral images.