EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers Exchanged

TL;DR

EfficientCD is a deep learning framework utilizing EfficientNet and novel modules like ChangeFPN for accurate and efficient change detection in remote sensing images, validated across multiple datasets.

Contribution

The paper introduces EfficientCD, a new change detection framework with a specialized ChangeFPN module and a feature upsampling method, improving accuracy and efficiency.

Findings

Outperforms existing methods on four datasets

Achieves high change detection accuracy

Demonstrates robustness across diverse remote sensing images

Abstract

With the widespread application of remote sensing technology in environmental monitoring, the demand for efficient and accurate remote sensing image change detection (CD) for natural environments is growing. We propose a novel deep learning framework named EfficientCD, specifically designed for remote sensing image change detection. The framework employs EfficientNet as its backbone network for feature extraction. To enhance the information exchange between bi-temporal image feature maps, we have designed a new Feature Pyramid Network module targeted at remote sensing change detection, named ChangeFPN. Additionally, to make full use of the multi-level feature maps in the decoding stage, we have developed a layer-by-layer feature upsampling module combined with Euclidean distance to improve feature fusion and reconstruction during the decoding stage. The EfficientCD has been experimentally validated on four remote sensing datasets: LEVIR-CD, SYSU-CD, CLCD, and WHUCD. The experimental results demonstrate that EfficientCD exhibits outstanding performance in change detection accuracy. The code and pretrained models will be released at https://github.com/dyzy41/mmrscd.