Semantic Multigranularity Feature Learning for High-Resolution Remote Sensing Image Scene Classification

• Published in: Applied sciences 2021-10, Vol.11 (19), p.9204
• Main Authors: Ma, Xinyi, Xiao, Zhifeng, Yun, Hong-sik, Lee, Seung-Jun
• Format: Article
• Language: English
• Subjects: Classification; Comparative studies; Cooperative learning; Datasets; Feature maps; fine-grained; High resolution; Image classification; Learning; Localization; Methods; multigranularity; Neural networks; Remote sensing; scene classification; Semantics; Spatial data; Visual tasks
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11199204

High-resolution remote sensing image scene classification is a challenging visual task due to the large intravariance and small intervariance between the categories. To accurately recognize the scene categories, it is essential to learn discriminative features from both global and local critical regions. Recent efforts focus on how to encourage the network to learn multigranularity features with the destruction of the spatial information on the input image at different scales, which leads to meaningless edges that are harmful to training. In this study, we propose a novel method named Semantic Multigranularity Feature Learning Network (SMGFL-Net) for remote sensing image scene classification. The core idea is to learn both global and multigranularity local features from rearranged intermediate feature maps, thus, eliminating the meaningless edges. These features are then fused for the final prediction. Our proposed framework is compared with a collection of state-of-the-art (SOTA) methods on two fine-grained remote sensing image scene datasets, including the NWPU-RESISC45 and Aerial Image Datasets (AID). We justify several design choices, including the branch granularities, fusion strategies, pooling operations, and necessity of feature map rearrangement through a comparative study. Moreover, the overall performance results show that SMGFL-Net consistently outperforms other peer methods in classification accuracy, and the superiority is more apparent with less training data, demonstrating the efficacy of feature learning of our approach.