Edge Feature Enhancement for Fine-Grained Segmentation of Remote Sensing Images

Fine-grained segmentation of remote sensing mineral images plays a crucial role in the investigation and monitoring of mineral resource. In deep-learning methods, fine-grained segmentation and edge detection are closely related in both data construction and feature extraction. However, the open-pit mineral areas in remote sensing images are heavily affected by complex natural environmental interference, posing challenges for precise data annotation and dataset construction. In view of this, we introduce the fine-annotated remote sensing mineral image (Fine-RSMI) dataset, which includes a total of 10225 images with finely annotated edges, while also introducing challenges such as multiscale and edge irregularities. To tackle the challenge of fine-grained segmentation in irregular edges, we propose a hierarchical fusion edge feature enhancement framework. Our framework consists of an edge detail feature enhancement module (EDFEM) and an edge supervision module (ESM). EDFEM vertically cascades multiple feature fusion units to obtain high-order complementary information for refining edge features. ESM further supervises network reinforcement learning of mineral area edges using ground truth edge maps to improve edge segmentation performance. Both modules work in a plug-and-play manner, enabling effortless integration into existing segmentation networks. Our method achieves further performance improvement in many general remote sensing segmentation frameworks, reaching the best results of 74.12% mean intersection-over-union (mIoU) on Fine-RSMI dataset and 78.64% mean accuracy (mAcc) on WHDLD dataset. Fine-RSMI dataset and code will be available at
https://github.com/chenmu1204/czx
.