Remote Sensing Image Scene Classification via Regional Growth-Based Key Area Fine Location and Multilayer Feature Fusion

Remote sensing image scene classification (RSISC) is important for analyzing and interpreting remote sensing images (RSIs). However, the intraclass difference and interclass similarity problems caused by complex backgrounds and variable scales bring great challenges for the effective classification of RSI scenes. In this letter, we solve the above problem mainly from two aspects. First, a multilayer feature fusion (MLFF) module is proposed to improve the classification performance of the networks by adaptively fusing multilayer semantic information. Then, to learn more discriminative local fine-grained, a regional growth-based key area (RGKA) fine location algorithm is proposed to accurately obtain local key areas. Finally, a two-branch network is utilized to complete the classification. Experiments are conducted on three publicly available datasets. The experimental results show that the proposed method outperforms most state-of-the-art methods and can considerably improve the accuracy of RSISC.