Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions

• URL: http://arxiv.org/abs/2103.00188v1
• Date: Sat, 27 Feb 2021 11:17:40 GMT
• Title: Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions
• Authors: Mengxi Liu, Qian Shi, Andrea Marinoni, Da He, Xiaoping Liu, Liangpei Zhang
• Abstract summary: Change detection plays a vital role in ecological protection and urban planning. Traditional subpixel-based methods for change detection using images with different resolutions may lead to substantial error accumulation. We propose a super-resolution-based change detection network (SRCDNet) with a stacked attention module.
• Score: 20.88671966047938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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