{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:39:50Z","timestamp":1760233190397,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,25]],"date-time":"2022-12-25T00:00:00Z","timestamp":1671926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"High Resolution Satellite Project of the State Administration of Science, Technology and Industry for National Defense of PRC","award":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"],"award-info":[{"award-number":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"]}]},{"name":"National Science and Technology Platform Construction","award":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"],"award-info":[{"award-number":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"]}]},{"name":"Key Projects of National Regional Innovation Joint Fund","award":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"],"award-info":[{"award-number":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"]}]},{"name":"Ministry of Education","award":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"],"award-info":[{"award-number":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"]}]},{"name":"Open Program of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Henan Province","award":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"],"award-info":[{"award-number":["80-Y50G19-9001-22\/23","2005DKA32300","U21A2014","16JJD770019","G202006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The performance of deep neural networks depends on the accuracy of labeled samples, as they usually contain label noise. This study examines the semantic segmentation of remote sensing images that include label noise and proposes an anti-label-noise network framework, termed Labeled Noise Robust Network in Remote Sensing Image Semantic Segmentation (NRN-RSSEG), to combat label noise. The algorithm combines three main components: network, attention mechanism, and a noise-robust loss function. Three different noise rates (containing both symmetric and asymmetric noise) were simulated to test the noise resistance of the network. Validation was performed in the Vaihingen region of the ISPRS Vaihingen 2D semantic labeling dataset, and the performance of the network was evaluated by comparing the NRN-RSSEG with the original U-Net model. The results show that NRN-RSSEG maintains a high accuracy on both clean and noisy datasets. Specifically, NRN-RSSEG outperforms UNET in terms of PA, MPA, Kappa, Mean_F1, and FWIoU in the presence of noisy datasets, and as the noise rate increases, each performance of UNET shows a decreasing trend while the performance of NRN-RSSEG decreases slowly and some performances show an increasing trend. At a noise rate of 0.5, the PA (\u22126.14%), MPA (\u22124.27%) Kappa (\u22128.55%), Mean_F1 (\u22125.11%), and FWIOU (\u22129.75%) of UNET degrade faster; while the PA (\u22122.51%), Kappa (\u22123.33%), and FWIoU of NRN-RSSEG (\u22123.26) degraded more slowly, MPA (+1.41) and Mean_F1 (+2.69%) showed an increasing trend. Furthermore, comparing the proposed model with the baseline method, the results demonstrate that the proposed NRN-RSSEG anti-noise framework can effectively help the current segmentation model to overcome the adverse effects of noisy label training.<\/jats:p>","DOI":"10.3390\/rs15010108","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:50:01Z","timestamp":1672109401000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["NRN-RSSEG: A Deep Neural Network Model for Combating Label Noise in Semantic Segmentation of Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3001-5791","authenticated-orcid":false,"given":"Mengfei","family":"Xi","sequence":"first","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatial-Temporal Big Data, Henan University, Kaifeng 475004, China"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatial-Temporal Big Data, Henan University, Kaifeng 475004, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6079-5597","authenticated-orcid":false,"given":"Zhilin","family":"He","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatial-Temporal Big Data, Henan University, Kaifeng 475004, China"}]},{"given":"Minmin","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatial-Temporal Big Data, Henan University, Kaifeng 475004, China"}]},{"given":"Fen","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatial-Temporal Big Data, Henan University, Kaifeng 475004, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/01431160903439882","article-title":"Information fusion of aerial images and LIDAR data in urban areas: Vector-stacking, re-classification and post-processing approaches","volume":"32","author":"Huang","year":"2011","journal-title":"Int. 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