Channel reconstruction and dual attention dynamic fusion for remote sensing image semantic segmentation
Xin Wang, Longxing Niu, Zhiwen Zheng, Qun Yang, Jia Lu, Hao Yang, Qin Qin, Guan Lian, Jiawei Wang
TL;DR
This paper introduces CRDFNet, a new network for remote sensing image segmentation that improves accuracy by combining global and local features.
Contribution
The novel CRDFNet integrates channel reconstruction and dual attention dynamic fusion for better segmentation of complex remote sensing images.
Findings
CRDFNet outperforms existing methods on multiple datasets in terms of F1 score, OA, and mIoU.
The channel feature aggregation module enhances high-resolution detail features for complex boundary shapes.
The dual attention feature refinement module improves small target segmentation accuracy.
Abstract
As the spatial resolution of remote sensing imagery continues to be improved, the complexity of the information also increases. Remote sensing images generally have characteristics such as wide imaging ranges, dispersed distribution of similar land objects, complex boundary shapes, and dense small targets, which pose severe challenges to semantic segmentation tasks. To address these challenges, we propose a channel reconstruction and dual attention dynamic fusion network (CRDFNet), which is a semantic segmentation network for remote sensing image that can effectively integrate global and local contexts. To better handle complex boundary shapes, we designed a channel feature aggregation module (CFAM), which can extract spatially redundant information during feature fusion and enhance high-resolution detail features. Through a channel reconstruction block, it promotes the alignment of…
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Taxonomy
TopicsAdvanced Neural Network Applications · Remote-Sensing Image Classification · Advanced Image Fusion Techniques