TITAN: A LighTweIght Temporal Attention Network for Remote Sensing Image Change Detection

Remote sensing change detection aims to identify significant variations in aerial image acquisition during different time frames. A decisive change detector is necessary to filter out the interest regions, such as recent urban buildings and changed vegetation, from undesired detections, i.e., artifacts generated by misregistration and illumination changes. To overcome common change detection problems (false positive and false negative alarms) and also processing overhead, this manuscript proposes a lighTweIght temporal attention network, aka TITAN, which comprises a partial-siamese deep learning-based change detector that leverages the natural capacity of an encoder-decoder framework to extract different levels of feature information from its input data. To assist the process of combining the meaningful encoded spatial-temporal information with its corresponding semantic decoded counterpart, we also propose the temporal change attention module (TCAM). Although TCAM does not explicitly account for nonlocal spatial changes, results support the claim that it implicitly helped TITAN in the matter. The experimental results show the proposed approach overcomes three out of four state-of-the-art techniques in terms of overall average F-measure, Intersection over Union (IOU), and Percentage of Wrong Classification (PWC) measures calculated over SZATAKI, Onera, LEVIR, and SYSU-CD remote sensing change detection datasets, with the lowest overhead.

Automated summary

This paper introduces a lightweight temporal attention network, TITAN, to address false positive and false negative alarms in change detection and reduce processing overhead. Experimental results show that the proposed approach outperforms other techniques in various evaluation metrics.