S2Looking: A Satellite Side-Looking Dataset for Building Change Detection

Remote Sensing

卷:13

Li Shen¹  Yao Lu¹  Bitao Jiang¹  Shouye Lv¹  Hao Chen²  Donghai Xie³  Jiabao Yue³  Hao Wei⁴  Rui Chen⁴ 

[1] Beijing Institute of Remote Sensing, Beijing 100011, China;

[2] Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China;

[3] Institute of Resource and Environment, Capital Normal University, Beijing 100048, China;

[4] School of Microelectronics, Tianjin University, Tianjin 300072, China;

关键词: change detection;    remote sensing;    benchmark dataset;    neural networks;   

DOI  :  10.3390/rs13245094

来源: DOAJ

【 摘 要 】

Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate the use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms.

【 授权许可】

Unknown