A 2 -FPN for semantic segmentation of fine-resolution remotely sensed images

作者：Rui Li, Libo Wang, Ce Zhang, Chenxi Duan, Shunyi Zheng · 发表于：International Journal of Remote Sensing · 年份：2022 · DOI：10.1080/01431161.2022.2030071 · 被引用次数：203 · 研究领域：Advanced Neural Network Applications、Remote-Sensing Image Classification、Advanced Image and Video Retrieval Techniques

The thriving development of earth observation technology makes more and more high-resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the huge spatial and spectral complexity leads to the automation of semantic segmentation becoming a challenging task. Addressing such an issue represents an exciting research field, which paves the way for scene-level landscape pattern analysis and decision-making. To tackle this problem, we propose an approach for automatic land segmentation based on the Feature Pyramid Network (FPN). As a classic architecture, FPN can build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion hinder FPN from further aggregating more discriminative features. Hence, we propose an Attention Aggregation Module (AAM) to enhance multiscale feature learning through attention-guided feature aggregation. Based on FPN and AAM, a novel framework named Attention Aggregation Feature Pyramid Network (A2-FPN) is developed for semantic segmentation of fine-resolution remotely sensed images. Extensive experiments conducted on four datasets demonstrate the effectiveness of our A2-FPN in segmentation accuracy. Code is available at https://github.com/lironui/A2-FPN.