SSFNet: Spectral–Spatial Fusion Network for Hyperspectral Remote Sensing Scene Classification

Remote sensing scene classification (RSSC) plays a vital role in a variety of applications and has attracted much more attention. In recent years, much progress has been made to release diverse datasets or develop all kinds of techniques for scene classification of multispectral remote sensing images. Nevertheless, very few studies have focused on hyperspectral image scene classification. Moreover, the existing scene classification approaches fail to fully employ the rich spectral information of the input images, which cannot achieve satisfactory performance for hyperspectral images. To alleviate these issues, this work proposes a spectral-spatial fusion network (SSFNet) for hyperspectral RSSC (HRSSC). First, a multiscale regional growth search (MSRGS) method is designed to extract salient object regions from the hyperspectral remote sensing scene. Then, a three-stream network architecture is proposed to extract the global spatial, local spatial, and spectral features, respectively. Finally, the fully connected layer is performed on the extracted features to obtain a class score followed by a decision fusion scheme to generate the final classification result. To evaluate the effectiveness of the proposed SSFNet, we created a publicly available benchmark for the HRSSC dataset, which contains 1445 hyperspectral images, covering 11 scene classes. Experiments on the HRSSC database claim that the proposed SSFNet can attain superior classification performance with respect to other state-of-the-art scene classification techniques. The code of the proposed SSFNet will be available at https://github.com/PuhongDuan/SSFNet.