{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:49:20Z","timestamp":1775472560351,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The segmentation of cloud and snow in satellite images is a key step for subsequent image analysis, interpretation, and other applications. In this paper, a cloud and snow segmentation method based on a deep convolutional neural network (DCNN) with enhanced encoder\u2013decoder architecture\u2014ED-CNN\u2014is proposed. In this method, the atrous spatial pyramid pooling (ASPP) module is used to enhance the encoder, while the decoder is enhanced with the fusion of features from different stages of the encoder, which improves the segmentation accuracy. Comparative experiments show that the proposed method is superior to DeepLabV3+ with Xception and ResNet50. Additionally, a rough-labeled dataset containing 23,520 images and fine-labeled data consisting of 310 images from the TH-1 satellite are created, where we studied the relationship between the quality and quantity of labels and the performance of cloud and snow segmentation. Through experiments on the same network with different datasets, we found that the cloud and snow segmentation performance is related more closely to the quantity of labels rather than their quality. Namely, under the same labeling consumption, using rough-labeled images only performs better than rough-labeled images plus 10% fine-labeled images.<\/jats:p>","DOI":"10.3390\/ijgi10070462","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:36:44Z","timestamp":1625571404000},"page":"462","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Cloud and Snow Segmentation in Satellite Images Using an Encoder\u2013Decoder Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"10","author":[{"given":"Kai","family":"Zheng","sequence":"first","affiliation":[{"name":"Institute of Surveying and Mapping, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiansheng","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Surveying and Mapping, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Surveying and Mapping, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xucheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Surveying and Mapping, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Surveying and Mapping, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. 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