基于SSAE深度学习特征表示的高光谱遥感图像分类方法

Hyperspectral Remote Sensing Image Classification Method Based on SSAE Deep Learning Feature Representation

摘要:

针对遥感图像中高光谱数据的分类问题,提出一种基于堆叠稀疏自动编码器(SSAE)深度学习特征表示的高光谱遥感图像分类方法。首先,将光谱数据样本进行预处理和归一化。然后,将其输入到SSAE中进行特征表示学习,并通过网格搜索来获得最优网络参数,以此获得有效的特征表示。最后通过支持向量机(SVM)分类器对输入图像特征进行分类,最终实现遥感图像中像素的分类。在两个标准数据集上的实验结果表明,该方法能够实现准确的高光谱地物分类。

Abstract:

Aiming at the classification problem of hyperspectral data in remote sensing images, a hyperspectral remote sensing image classification method based on the deep learning feature representation by stacked sparse auto-encoder (SSAE) is proposed. First, the spectral data samples are pre-processed and normalized. Then, it is input into the SSAE for feature representation learning, and the grid search is used to obtain the optimal network parameters, thereby obtaining a valid feature representation. Finally, the input image features are classified by the support vector machine (SVM) classifier, and finally the pixels in the remote sensing image are classified. Experimental results on two standard datasets show that this method can achieve accurate hyperspectral landmark classification.