计算机科学
变更检测
鉴别器
人工智能
相似性(几何)
特征(语言学)
块(置换群论)
图像(数学)
公制(单位)
计算机视觉
模式识别(心理学)
数学
探测器
几何学
哲学
电信
经济
语言学
运营管理
作者
Mengxi Liu,Qian Shi,Andrea Marinoni,Da He,Xiaoping Li,Liangpei Zhang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-18
被引量:53
标识
DOI:10.1109/tgrs.2021.3091758
摘要
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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