EHDCD: An Edge Enhanced Hierarchical Dual Gated Network for Forest-Cropland Change Detection
Tingting Zhao, Yicong Sun, Xia Yu, Liqian Zhang, Quanping Zhang, Yunli Bai

TL;DR
This paper introduces a new model for detecting changes between forest and cropland in satellite images, improving accuracy by focusing on edges and reducing errors.
Contribution
The EHDCD model introduces edge enhancement, dynamic adaptation, and feature compensation for better change detection in forest-cropland transitions.
Findings
The model achieved an F1 score of 89.06% on the FC-CD dataset.
It outperformed existing methods on CLCD and SYSU-CD with scores of 83.37% and 85.06%.
The model effectively reduces noise and improves edge recognition for accurate monitoring.
Abstract
Aiming at the differences in spatial spectral attributes between forested land and cultivated land on remote sensing images, and the deficiencies of existing remote sensing change detection methods that are difficult to capture fine edge structures and distinguish pseudo changes, this paper introduces an Edge Enhanced Hierarchical Dual Gated Change Detection (EHDCD) model for forested land and cultivated land, aiming to meet the demand for representing the complex features of these two land types. The model designs an Edge Enhanced Channel Attention Module (EECA) to strengthen the edge recognition ability and suppress the noise interference; proposes a High-Low Level Dynamic Adaptation Strategy (HiLo) to realize the balanced expression of detail information and semantic features; and constructs a Dual Gated Feature Compensation Module (DGFM) to effectively reduce the misdetection rate…
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Taxonomy
TopicsRemote-Sensing Image Classification · Remote Sensing in Agriculture · Remote Sensing and Land Use
