Hyperspectral images classification with convolutional neural network and textural feature using limited training samples

In this letter, a new deep learning framework, which integrates textural features of gray level co-occurrence matrix (GLCM) into convolutional neural networks (CNNs) is proposed for hyperspectral images (HSIs) classification using limited number of labeled samples. The proposed method can be implemented in three steps. Firstly, the GLCM textural features are extracted from the first principal component after the principal components analysis (PCA) transformation. Secondly, a CNN is built to extract the deep spectral features from the original HSIs, and the features are concatenated with the textural features obtained in the first step in a concat layer of CNN. Finally, softmax is employed to generate classification maps at the end of the framework. In this way, the CNN focuses on the learning of spectral features only, and the generated textural features are used directly as one set of features before softmax. These lead to the reduction of the requirements for the size of training samples and the improvement of computing efficiency. The experimental results are presented for three HSIs and compared with several advanced deep learning and spectral-spatial classification techniques. The competitive classification accuracy can be obtained, especially when only a limited number of training samples are available.