{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T06:07:05Z","timestamp":1768457225792,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,20]],"date-time":"2024-04-20T00:00:00Z","timestamp":1713571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671010"],"award-info":[{"award-number":["61671010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["B2018Q03"],"award-info":[{"award-number":["B2018Q03"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Qing Lan Project of Jiangsu Province","award":["61671010"],"award-info":[{"award-number":["61671010"]}]},{"name":"Qing Lan Project of Jiangsu Province","award":["B2018Q03"],"award-info":[{"award-number":["B2018Q03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The effective segmentation of clouds and cloud shadows is crucial for surface feature extraction, climate monitoring, and atmospheric correction, but it remains a critical challenge in remote sensing image processing. Cloud features are intricate, with varied distributions and unclear boundaries, making accurate extraction difficult, with only a few networks addressing this challenge. To tackle these issues, we introduce a multi-scale receptive field aggregation network (MRFA-Net). The MRFA-Net comprises an MRFA-Encoder and MRFA-Decoder. Within the encoder, the net includes the asymmetric feature extractor module (AFEM) and multi-scale attention, which capture diverse local features and enhance contextual semantic understanding, respectively. The MRFA-Decoder includes the multi-path decoder module (MDM) for blending features and the global feature refinement module (GFRM) for optimizing information via learnable matrix decomposition. Experimental results demonstrate that our model excelled in generalization and segmentation performance when addressing various complex backgrounds and different category detections, exhibiting advantages in terms of parameter efficiency and computational complexity, with the MRFA-Net achieving a mean intersection over union (MIoU) of 94.12% on our custom Cloud and Shadow dataset, and 87.54% on the open-source HRC_WHU dataset, outperforming other models by at least 0.53% and 0.62%. The proposed model demonstrates applicability in practical scenarios where features are difficult to distinguish.<\/jats:p>","DOI":"10.3390\/rs16081456","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T03:57:07Z","timestamp":1713758227000},"page":"1456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["MRFA-Net: Multi-Scale Receptive Feature Aggregation Network for Cloud and Shadow Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Jianxiang","family":"Wang","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"College of Automation, Nanjing University of Information Science and Technology, Nanjing 211004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanlu","family":"Li","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"College of Automation, Nanjing University of Information Science and Technology, Nanjing 211004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoting","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Automation, Nanjing University of Information Science and Technology, Nanjing 211004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Automation, Nanjing University of Information Science and Technology, Nanjing 211004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxuan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Automation, Nanjing University of Information Science and Technology, Nanjing 211004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1175\/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2","article-title":"Advances in Understanding Clouds from ISCCP","volume":"80","author":"Rossow","year":"1999","journal-title":"J. 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