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Change detection, which aims to distinguish surface changes based onbi-temporal images, plays a vital role in ecological protection and urbanplanning. Since high resolution (HR) images cannot be typically acquiredcontinuously over time, bi-temporal images with different resolutions are oftenadopted for change detection in practical applications. Traditionalsubpixel-based methods for change detection using images with differentresolutions may lead to substantial error accumulation when HR images areemployed; this is because of intraclass heterogeneity and interclasssimilarity. Therefore, it is necessary to develop a novel method for changedetection using images with different resolutions, that is more suitable for HRimages. To this end, we propose a super-resolution-based change detectionnetwork (SRCDNet) with a stacked attention module. The SRCDNet employs a superresolution (SR) module containing a generator and a discriminator to directlylearn SR images through adversarial learning and overcome the resolutiondifference between bi-temporal images. To enhance the useful information inmulti-scale features, a stacked attention module consisting of fiveconvolutional block attention modules (CBAMs) is integrated to the featureextractor. The final change map is obtained through a metric learning-basedchange decision module, wherein a distance map between bi-temporal features iscalculated. The experimental results demonstrate the superiority of theproposed method, which not only outperforms all baselines -with the highest F1scores of 87.40% on the building change detection dataset and 92.94% on thechange detection dataset -but also obtains the best accuracies on experimentsperformed with images having a 4x and 8x resolution difference. The source codeof SRCDNet will be available at https://github.com/liumency/SRCDNet.

Super-Resolution-Based Change Detection Network With Stacked Attention Module for Images With Different Resolutions