Remote Sensing Image Scene Classification With Multi-View Collaborative Representation Network

The utilization of deep learning methods in remote sensing image scene classification (RSISC) has gained significant attention, showcasing remarkable performance. However, these methods rely solely on the network for automatic weight assignment learning, which may introduce biases in attention calculations for remote sensing images. To address this issue, we propose a multi-view collaborative representation network (MCRNet) for RSISC. Specifically, we introduce a multiview collaborative representation framework (MCRF) to evaluate the impact of local features on key information within global features by different data augmentation. Furthermore, the introduction of a semantic summarization dictionary (SSD) aims to enhance the reconstruction of global semantic features through the optimization of a low-redundancy dictionary. Experiment results on two publicly available datasets confirm that the proposed model effectively improves the classification performance.