Frequency-Driven Edge Guidance Network for Semantic Segmentation of Remote Sensing Images

Abstract

Semantic segmentation plays a significant role in parsing remote sensing images. However, mainstream segmentation models lack a thorough understanding of the complex structures and scale differences, and struggle to effectively locate and emphasize diverse edges. Aiming at these limitations, we propose a frequency-driven edge guidance network, named FDEG-Net, for semantic segmentation of remote sensing images. First, we design a joint sparse context aggregation module that integrates both dense local context and sparse long-range context to improve the analysis of intricate and multiscale objects. Second, an edge guidance module is developed for strong interclass edge acquisition. It applies a 2-D discrete wavelet transform, coefficient superposition method, and adaptive edge feature enhancement algorithm to reduce low-frequency information and highlight salient boundaries in spatial features. This module has two significant advantages. 1) The edge positions are defined in pixel intensity with high interpretability. 2) The modular design without additional edge labels is plug-and-play. The effectiveness and robustness of this module are validated through edge visualization results. The proposed FDEG-Net is evaluated on the Potsdam, Vaihingen, and GID datasets, demonstrating its excellent performance in accurately capturing the rich semantics of geographic space features.

Keywords

• Context extraction
• edge guidance
• remote sensing images (RSIs)
• semantic segmentation