Toward Efficient Remote Sensing Image Change Detection via Cross-Temporal Context Learning

Abstract

Change detection (CD) aims to find areas of specific changes in multi-temporal remote sensing images. The existing methods fail to adequately explore the cross-temporal global context, making the establishment of spatial-temporal deep global associations insufficient and inefficient. As a result, their performance is vulnerable to complex and various objects in changing scenes. Hence, we propose a cross-temporal context learning network, termed as CCLNet, where the intra- and inter-temporal long-range dependency are mined and interactively fused, to fully exploit the cross-temporal context information. Specifically, a lightweight convolutional neural network is first used to extract deep semantic features. Then, a well-designed cross-temporal fusion transformer (CFT) is proposed to locate the changing objects in the scene by establishing the long-range dependency across bitemporal images. Thanks to this, the temporal-specific information extraction and cross-temporal information integration are seamlessly integrated into the same network, thereby significantly improving the discriminative features of changing objects. Furthermore, this allows us using naive backbones with low computational cost to achieve reliable CD performance. Experiments on mainstream benchmarks show that our proposed method can handle CD task faster than state-of-the-art methods while maintaining better or comparable matching accuracy on a single RTX3090.