SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection

TL;DR

SRC-Net is a novel change detection network that effectively leverages bi-temporal spatial relationships through specialized modules, improving accuracy and robustness in remote sensing imagery analysis.

Contribution

The paper introduces SRC-Net with a Perception and Interaction Module and a Patch-Mode joint Feature Fusion Module, enhancing bi-temporal feature extraction and fusion by focusing on spatial relationships.

Findings

Outperforms state-of-the-art methods on LEVIR-CD and WHU datasets.

Maintains a modest parameter count while improving detection accuracy.

Demonstrates the effectiveness of spatial relationship modeling in change detection.

Abstract

Change detection (CD) in remote sensing imagery is a crucial task with applications in environmental monitoring, urban development, and disaster management. CD involves utilizing bi-temporal images to identify changes over time. The bi-temporal spatial relationships between features at the same location at different times play a key role in this process. However, existing change detection networks often do not fully leverage these spatial relationships during bi-temporal feature extraction and fusion. In this work, we propose SRC-Net: a bi-temporal spatial relationship concerned network for CD. The proposed SRC-Net includes a Perception and Interaction Module that incorporates spatial relationships and establishes a cross-branch perception mechanism to enhance the precision and robustness of feature extraction. Additionally, a Patch-Mode joint Feature Fusion Module is introduced to address information loss in current methods. It considers different change modes and concerns about spatial relationships, resulting in more expressive fusion features. Furthermore, we construct a novel network using these two relationship concerned modules and conducted experiments on the LEVIR-CD and WHU Building datasets. The experimental results demonstrate that our network outperforms state-of-the-art (SOTA) methods while maintaining a modest parameter count. We believe our approach sets a new paradigm for change detection and will inspire further advancements in the field. The code and models are publicly available at https://github.com/Chnja/SRCNet.