Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-05, Vol.14 (9), p.2263
• Main Authors: Xu, Gang, Fang, Yongjun, Deng, Min, Sun, Geng, Chen, Jie
• Format: Article
• Language: English
• Subjects: Accumulation; Accuracy; Classification; Datasets; Deep learning; Fault tolerance; fault-tolerant learning; Image classification; Image retrieval; Labeling; Land cover; Land use; Learning; Machine learning; Mapping; Neural networks; Noise; noise label; Remote sensing; urban mapping; Urbanization
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14092263

China’s urbanization has dramatically accelerated in recent decades. Land for urban build-up has changed not only in large cities but also in small counties. Land cover mapping is one of the fundamental tasks in the field of remote sensing and has received great attention. However, most current mapping requires a significant manual effort for labeling or classification. It is of great practical value to use the existing low-resolution label data for the classification of higher resolution images. In this regard, this work proposes a method based on noise-label learning for fine-grained mapping of urban build-up land in a county in central China. Specifically, this work produces a build-up land map with a resolution of 10 m based on a land cover map with a resolution of 30 m. Experimental results show that the accuracy of the results is improved by 5.5% compared with that of the baseline method. This notion indicates that the time required to produce a fine land cover map can be significantly reduced using existing coarse-grained data.