Deep CNN-based hyperspectral image classification using discriminative multiple spatial-spectral feature fusion.

Convolutional Neural Networks (CNNs) are widely used in various fields, and have shown good performance in hyperspectral image (HSI) classification. Recently, utilizing deep networks to learn spatial-spectral features has become of great interest. However, excessively increasing the depth of network may result in overfitting. Moreover, in HSI classification, the existing network models ignore the strong complementary yet correlated spatial-spectral information among different hierarchical layers. In order to address these two problems, a novel CNN-based method for HSI classification is proposed. Firstly, it considers fusing the outputs of recurrent two layers in each large convolutional block and thereby using the fusion result as the input of next layer, which facilitates the extraction of discriminative features. Next, the spectral-spatial features are extracted by cascading spectral features to four-scale spatial features from shallow to deep layers. Finally, a 1 × 1 convolution layer is used to interact and integrate information across channels. Without increasing the number of training samples and the size of pixel patches at the training stage, the proposed approach achieves the state-of-the-art results in the experiment on three well-known hyperspectral images. [ABSTRACT FROM AUTHOR]