Relationship Learning From Multisource Images via Spatial-Spectral Perception Network
Yunhao Gao, Wei Li, Junjie Wang, Mengmeng Zhang, Ran Tao
Abstract
Advances in multisource remote sensing have allowed for the development of more comprehensive observation. The adoption of deep convolutional neural networks (CNN) naturally includes spatial-spectral information, which has achieved promising performance in multisource data classification. However, challenges are still found with the extraction of spatial distribution and spectrum relationships, which eventually limit the classification performance. To solve the issue, a spatial-spectral perception network (S2PNet) is proposed to extract the advantages of different data sources and the cross information between data sources in a targeted manner. Specifically, the spatial perception network is developed to build the spatial distribution relationship from high-resolution images, while the spectral perception network extracts the spectrum relationship from spectral images. For perceiving cross information, a memory unit is utilized to store the features from different data sources in succession. In addition, the distance loss and reconstruction loss are introduced to keep the feature integrity, and the cross-entropy loss ensures that features can distinguish different classes. The comprehensive experiments are conducted on several datasets to validate the superiority of the proposed algorithm. The proposed S2PNet outperforms the considered classifiers with an average improvement of +0.77%, +5.62%, +1.58%, and +1.79% for overall accuracy values.