  Change detection, which aims to distinguish surface changes based on
bi-temporal images, plays a vital role in ecological protection and urban
planning. Since high resolution (HR) images cannot be typically acquired
continuously over time, bi-temporal images with different resolutions are often
adopted for change detection in practical applications. Traditional
subpixel-based methods for change detection using images with different
resolutions may lead to substantial error accumulation when HR images are
employed; this is because of intraclass heterogeneity and interclass
similarity. Therefore, it is necessary to develop a novel method for change
detection using images with different resolutions, that is more suitable for HR
images. To this end, we propose a super-resolution-based change detection
network (SRCDNet) with a stacked attention module. The SRCDNet employs a super
resolution (SR) module containing a generator and a discriminator to directly
learn SR images through adversarial learning and overcome the resolution
difference between bi-temporal images. To enhance the useful information in
multi-scale features, a stacked attention module consisting of five
convolutional block attention modules (CBAMs) is integrated to the feature
extractor. The final change map is obtained through a metric learning-based
change decision module, wherein a distance map between bi-temporal features is
calculated. The experimental results demonstrate the superiority of the
proposed method, which not only outperforms all baselines -with the highest F1
scores of 87.40% on the building change detection dataset and 92.94% on the
change detection dataset -but also obtains the best accuracies on experiments
performed with images having a 4x and 8x resolution difference. The source code
of SRCDNet will be available at https://github.com/liumency/SRCDNet.
