Construction of Multi-Scale Fusion Attention Unified Perceptual Parsing Networks for Semantic Segmentation of Mangrove Remote Sensing Images - Xin Wang; Yu Zhang; Wenquan Xu; Hanxi Wang; Jingye Cai; Qin Qin; Qin Wang; Jing Zeng (Applied Sciences)
Mangrove forests play a crucial role in coastal ecosystem protection and carbon sequestration processes. However, monitoring remains challenging due to the forests’ complex spatial distribution characteristics. This study addresses three key challenges in mangrove monitoring: limited high-quality datasets, the complex spatial characteristics of mangrove distribution, and technical difficulties in high-resolution image processing. To address these challenges, we present two main contributions. (1) Using multi-source high-resolution satellite imagery from China’s new generation of Earth observation satellites, we constructed the Mangrove Semantic Segmentation Dataset of Beihai, Guangxi (MSSDBG); (2) We propose a novel Multi-scale Fusion Attention Unified Perceptual Network (MFA-UperNet) for precise mangrove segmentation. This network integrates Cascade Pyramid Fusion Modules, a Multi-scale Selective Kernel Attention Module, and an Auxiliary Edge Neck to process the unique characteristics of mangrove remote sensing images, particularly addressing issues of scale variation, complex backgrounds, and boundary accuracy. The experimental results demonstrate that our approach achieved a mean Intersection over Union (mIoU) of 94.54% and a mean Pixel Accuracy (mPA) of 97.14% on the MSSDBG dataset, significantly outperforming existing methods. This study provides valuable tools and methods for monitoring and protecting mangrove ecosystems, contributing to the preservation of these critical coastal environments.