Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation
| Autor: | Ji Hong Cheng, Ming Hsun Ko, Pei-Yin Chen, Chien Chih Lai, Cheng Chien Liu, Yu Cheng Zhang, Yi Hsin Chen |
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| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
deep residual learning Computer science Science Feature extraction 0211 other engineering and technologies Cloud computing 02 engineering and technology Land cover land use and land cover 01 natural sciences atrous convolution cloud classification CloudNet change detection 021101 geological & geomatics engineering 0105 earth and related environmental sciences Pixel business.industry Deep learning deep learning Pattern recognition semantic image segmentation Sentinel-2 Identification (information) General Earth and Planetary Sciences Cirrus Artificial intelligence business Change detection |
| Zdroj: | Remote Sensing; Volume 11; Issue 2; Pages: 119 Remote Sensing, Vol 11, Iss 2, p 119 (2019) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs11020119 |
| Popis: | Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive survey of DL in RS has been reviewed, and the RS community has been suggested to be leading researchers in DL. Based on deep residual learning, semantic image segmentation, and the concept of atrous convolution, we propose a new DL architecture, named CloudNet, with an enhanced capability of feature extraction for classifying cloud and haze from Sentinel-2 imagery, with the intention of supporting automatic change detection in LULC. To ensure the quality of the training dataset, scene classification maps of Taiwan processed by Sen2cor were visually examined and edited, resulting in a total of 12,769 sub-images with a standard size of 224 × 224 pixels, cut from the Sen2cor-corrected images and compiled in a trainset. The data augmentation technique enabled CloudNet to have stable cirrus identification capability without extensive training data. Compared to the traditional method and other DL methods, CloudNet had higher accuracy in cloud and haze classification, as well as better performance in cirrus cloud recognition. CloudNet will be incorporated into the Open Access Satellite Image Service to facilitate change detection by using Sentinel-2 imagery on a regular and automatic basis. |
| Databáze: | OpenAIRE |
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