CDLNet: Collaborative Dictionary Learning Network for Remote Sensing Image Scene Classification

In recent years, deep learning-based methods have been extensively utilized in remote sensing image scene classification and have achieved remarkable performance. The wide geographical coverage and resolution differences of scene images result in significant within-class diversity and between-class similarity, hindering the further improvement of classification accuracy. Attention-based methods automatically estimate the importance of local regions by learning weight assignments, which effectively enhance the feature extraction capability of the network. However, methods that solely rely on the network to automatically learn weight assignments may introduce biases in the attention calculations. By analyzing the specific contribution of local features to the key components of global semantics, we propose a collaborative dictionary learning network (CDLNet). CDLNet utilizes the collaborative representation method to decompose global features into a set of key semantic vectors to guide the attention learning process of the network. Specifically, we design a semantic summarization module (SSM), which reconstructs global semantic features by optimizing a low-redundancy dictionary. Next, we propose a global semantic attention module (GSAM), which calculates the contribution of local features to the global feature key information based on their correlation with the reconstructed key semantic set. Finally, an attention transfer loss is introduced to further enhance the attention of low-level feature maps. The experimental results on three publicly available datasets demonstrate that CDLNet can effectively improve within-class diversity and between-class similarity by optimizing the attention learning of the network, thereby achieving great promotion in comparison with state-of-the-art methods. The implementation is publicly available at https://github.com/liuofficial/CDLNet.