计算机科学
变更检测
水准点(测量)
特征(语言学)
判别式
模式识别(心理学)
编码器
人工智能
遥感
大地测量学
语言学
操作系统
地质学
哲学
地理
作者
Ziming Li,Chenxi Yan,Ying Sun,Qinchuan Xin
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:69
标识
DOI:10.1109/tgrs.2022.3159544
摘要
Detecting changes using bitemporal remote sensing imagery is vital to understand the dynamics of the land surface. Existing change detection models based on deep learning suffer from the problem of scale variation and pseudochange due to their insufficient multilevel aggregation and inadequate capability of feature representation, which limits the accuracy. This study proposes a densely attentive refinement network (DARNet) to improve change detection on bitemporal very-high-resolution remote sensing images. DARNet is based on the U-shape encoder–decoder architecture with the Siamese network as a feature extractor. The dense skip connection module (DSCM) is employed between the decoder and the encoder to aggregate multilevel feature maps. The hybrid attention module (HAM) is integrated to exploit contextual information and generate discriminative features. The recurrent refinement module (RRM) is exploited to progressively refine the predicted change maps during the decoding process. Experiments on testing the model performance were conducted on three benchmark datasets: the season-varying change detection (SVCD) dataset, the Sun Yat-sen University change detection (SYSU-CD) dataset, and the Learning Vision and Remote Sensing Laboratory building change detection (LEVIR-CD) dataset. The experimental results demonstrate that DARNet outperforms state-of-the-art models with kappa of 96.58%, 75.35%, and 90.69% for the SVCD, SYSU-CD, and LEVIR-CD datasets, respectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI