Abstract: In order to avoid the problems that it is difficult and time-consuming to obtain enough labeled samples for hyperspectral image classification, while numerous unlabeled samples have not been used efficiently, and over-dependent on spectral information while ignoring spatial information, it proposes a spatial-spectral feature combined with active deep learning approach for hyperspectral image classification in this paper. Firstly, it extracts spatial-spectral feature by extracting a pixel’s small neighborhood pixels of a square as the spatial information and combines with the original spectral information on the basis of principal component analysis to the original image for dimension reduction. Secondly, it creates a sparse expression of raw hyperspectral image using sparse autoencoder. Then a deep neural network generating the deep features of raw data is built through learning stacked sparse autoencoder layer by layer. In addition, limited labeled samples are used to train the softmax classifier and fine-tune all the stacked sparse autoencoders in a supervised way. Finally, the most uncertain samples are selected to label with active learning algorithm and added to the training samples. Classification experiment on Pavia University image and Pavia Center image confirms that the proposed method can effectively improve the classification accuracy with less labeled samples comparing with traditional classification method.

Key words: hyperspectral image classification, spatial-spectral feature, stacked sparse autoencoders, active learning

摘要： 针对当前高光谱遥感影像分类人工标注样本费时费力，大量未标注样本未得到有效利用以及主要利用光谱信息而忽视空间信息等问题，提出了一种空-谱信息与主动深度学习相结合的高光谱影像分类方法。首先利用主成分分析对原始影像进行降维，在此基础上提取像素的一正方形小邻域作为该像素的空间信息并结合其原始光谱信息得到空谱特征。然后，通过稀疏自编码器得到原始数据的稀疏特征表达，并通过逐层无监督学习稀疏自编码器构建深度神经网络，输出原始数据的深度特征，将其连接到softmax分类器，利用少量标记样本以监督学习的方式完成模型的精调。最后，利用主动学习算法选择最不确定性样本对其进行标注，并加入至训练样本以提高分类器的分类效果。分别对PaviaU影像和PaviaC影像进行分类实验的结果表明，该方法在少量标记样本情况下，相对于传统方法能有效地提高分类精度。

关键词: 高光谱遥感影像分类, 空谱特征, 堆栈式稀疏自编码深度网络, 主动学习