期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 60, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3091758
关键词
Feature extraction; Remote sensing; Superresolution; Spatial resolution; Measurement; Semantics; Data mining; Change detection (CD); fully convolutional networks (FCNs); metric learning; remote sensing images; super-resolution
类别
资金
- Program for Guangdong Introducing Innovative and Entrepreneurial Teams [2017ZT07X355]
- Guangdong Natural Science Foundation [2019A1515011057]
- National Natural Science Foundation of China [61976234]
- Open research fund of National Key Laboratory of Surveying, Mapping and Remote Sensing Information Engineering, Wuhan University
- Guangzhou Applied Basic Research Project
- Center for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA)
- Research Council of Norway (RCN) [237906]
This study introduces a novel super-resolution-based change detection network (SRCDNet) with a stacked attention module (SAM) and convolutional block attention modules (CBAMs) to effectively address surface changes in high-resolution images.
Change detection (CD) aims to distinguish surface changes based on bitemporal images. Since high-resolution (HR) images cannot be typically acquired continuously over time, bitemporal images with different resolutions are often adopted for CD in practical applications. Traditional subpixel-based methods for CD using images with different resolutions may lead to substantial error accumulation when the HR images are employed, which is because of intraclass heterogeneity and interclass similarity. Therefore, it is necessary to develop a novel method for CD using images with different resolutions that are more suitable for the HR images. To this end, we propose a super-resolution-based change detection network (SRCDNet) with a stacked attention module (SAM). The SRCDNet employs a super-resolution (SR) module containing a generator and a discriminator to directly learn the SR images through adversarial learning and overcome the resolution difference between the bitemporal images. To enhance the useful information in multiscale features, a SAM 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 bitemporal features is calculated. Ablation study and comparative experiments on two large datasets, building change detection dataset (BCDD) and season-varying change detection dataset (CDD), and a real-image experiment on the Google dataset fully demonstrate the superiority of the proposed method. The source code of SRCDNet is available at https://github.com/liumency/SRCDNet.
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