EHDCD: An Edge Enhanced Hierarchical Dual Gated Network for Forest-Cropland Change Detection

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 of change detection. Experiments show that the F1 scores of the model on the self-constructed forest and agricultural dataset FC-CD and public datasets CLCD and SYSU-CD reach 89.06%, 83.37%, and 85.06%, respectively, which can more accurately support the dynamic monitoring applications of forest land and cropland.

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