A semantic-driven image scene fine-grained enhancement recognition

Scene classification for Remote sensing image has attracted great attention because of its difficulties and wide application. There exits several limitations for traditional CNN-based methods, such as insufficient feature extraction ability and complex target of remote sensing image features. In addition, the experimental data is based on the overhead view, which is characterized by fuzzy semantics, small differences between classes and significant differences within classes. To address those issues, we realize several classic network improvement methods such as transfer learning and introduce the attention mechanism Squeeze-and-Excitation (SE) module. We carry out the fine-grained analysis of the space-based view scene image, specifically using the progressive multi-granularity puzzle training for scene recognition. We also propose a semantic-driven scene fine-grained enhancement based on the classic classification network and the progressive multi-granularity puzzle training. To verify the effectiveness of the proposed semantic-driven scene fine-grained enhancement model, we conduct comparative experiments based on several widely used CNN models and a public remote sensing image scene classification data set, and achieve the state-of-the-art result on the data set.