{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T01:07:48Z","timestamp":1768266468721,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T00:00:00Z","timestamp":1527033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>It is a challenge to distinguish between different cloud types because of the complexity and diversity of cloud coverage, which is a significant clutter source that impacts on target detection and identification from the images of space-based infrared sensors. In this paper, a novel strategy for cloud classification in wide-swath passive sensor images is developed, which is aided by narrow-swath active sensor data. The strategy consists of three steps, that is, the orbit registration, most matching donor pixel selection, and cloud type assignment for each recipient pixel. A new criterion for orbit registration is proposed so as to improve the matching accuracy. The most matching donor pixel is selected via the Euclidean distance and the square sum of the radiance relative differences between the recipient and the potential donor pixels. Each recipient pixel is then assigned a cloud type that corresponds to the most matching donor. The cloud classification of the Moderate Resolution Imaging Spectroradiometer (MODIS) images is performed with the aid of the data from Cloud Profiling Radar (CPR). The results are compared with the CloudSat product 2B-CLDCLASS, as well as those that are obtained using the method of the International Satellite Cloud Climatology Project (ISCCP), which demonstrates the superior classification performance of the proposed strategy.<\/jats:p>","DOI":"10.3390\/rs10060812","type":"journal-article","created":{"date-parts":[[2018,5,24]],"date-time":"2018-05-24T02:55:43Z","timestamp":1527130543000},"page":"812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cloud Classification in Wide-Swath Passive Sensor Images Aided by Narrow-Swath Active Sensor Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7199-9785","authenticated-orcid":false,"given":"Hongxia","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, No. 37 Xueyuan Road, Beijing 100191, China"}]},{"given":"Xiaojian","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, No. 37 Xueyuan Road, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.3390\/rs6064907","article-title":"Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing","volume":"6","author":"Hughes","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5124","DOI":"10.3390\/rs6065124","article-title":"Daytime low stratiform cloud detection on AVHRR imagery","volume":"6","author":"Musial","year":"2014","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hollstein, A., Segl, K., Guanter, L., Brell, M., and Enesco, M. 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