Hyperspectral Remote Sensing Scene Classification with Spectral-Spatial Convolutional Network

Remote sensing scene classification has garnered significant interest. However, few studies are dedicated to the classifi-cation of hyperspectral remote sensing scenes. Furthermore, current approaches that are designed for multispectral or visible images are not suitable for hyperspectral remote sensing scene classification. To solve these issues, a spectral-spatial convolutional network is proposed for hyperspectral remote sensing scene classification. First, a multiscale regional growth search method is developed to extract object region of the input data. Then, a three-branch network is designed to extract local spatial, global spatial, and spectral features. Finally, the obtained features are fed into fully connected layer to obtain class scores followed by a decision fusion rule to yield the final classification result. To evaluate the effect of hyperspectral remote sensing scene classification, a hyper-spectral remote sensing scene classification dataset (HRSS-C) is constructed, which consists of 1445 hyperspectral images with 11 different classes. Experiments on the HRSS-C reveal that our method gains superior classification performance with respect to other advanced approaches.