{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:49:57Z","timestamp":1781020197200,"version":"3.54.1"},"reference-count":23,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"vor","delay-in-days":172,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurate land cover classification of urban aerial imagery presents significant challenges, particularly in recognising small objects and similar\u2010appearing features (e.g., flat land, prepared land for cultivation, crop growing areas and built\u2010up regions along with ground water resource areas). These challenges arise due to the irregular scaling of extracted features at various rates from complex urban scenes and the mismatch in feature information flow across channels, ultimately affecting the overall accuracy (OA) of the network. To address these issues, we propose the scale\u2010wise interaction fusion network (SIFN) for land cover classification of urban scene imagery. The SIFN comprises four key modules: multi\u2010scale feature extraction, scale\u2010wise interaction, feature shuffle\u2010fusion and adaptive mask generation. The multi\u2010scale feature extraction module captures contextual information across different dilation rates of convolutional layers, effectively handling varying object sizes. The scale\u2010wise interaction module enhances the learning of multi\u2010scale contextual features, while the feature shuffle\u2010fusion module facilitates cross\u2010scale information exchange, improving feature representation. Lastly, adaptive mask generation ensures precise boundary delineation and reduces misclassification in transitional zones. The proposed network significantly improves boundary masking accuracy for small and similar objects, thereby enhancing the overall land cover classification performance.<\/jats:p>","DOI":"10.1049\/ipr2.70139","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T00:09:36Z","timestamp":1750637376000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Scale\u2010Wise Interaction Fusion Network for Land Cover Classification of Urban Scene Imagery"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3724-6528","authenticated-orcid":false,"given":"Muhammad","family":"Shafiq","sequence":"first","affiliation":[{"name":"School of Information Engineering Qujing Normal University Qujing Yunnan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waeal J.","family":"Obidallah","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanrun","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Information Engineering Qujing Normal University Qujing Yunnan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anas","family":"Bilal","sequence":"additional","affiliation":[{"name":"College of Information Science &amp; Technology Hainan Normal University Haikou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yousef A.","family":"Alduraywish","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2025,6,22]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2019.111322"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2024.2302351"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3041645"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10991-0"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs10010073"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs11222719"},{"key":"e_1_2_11_8_1","doi-asserted-by":"crossref","unstructured":"X.Wang Y.Zhao D.Liu G.Sun A.Zhang andJ.Li \u201cA Lightweight and Multi\u2010Scale CNN Model for Land\u2010Cover Classification With High\u2010Resolution Remote Sensing Images \u201d in2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS(IEEE 2021) 6341\u20136344.","DOI":"10.1109\/IGARSS47720.2021.9553755"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.06.001"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14051844"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs12020207"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3238648"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2020.1871094"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14225862"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs12020311"},{"key":"e_1_2_11_16_1","doi-asserted-by":"crossref","unstructured":"I.Demir K.Koperski D.Lindenbaum et\u00a0al. \u201cDeepglobe 2018: A Challenge to Parse the Earth Through Satellite Images \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops(IEEE 2018) 172\u2013181.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3310160"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs11010088"},{"key":"e_1_2_11_19_1","doi-asserted-by":"crossref","unstructured":"R. S.Puttagunta Z.Li S.Bhattacharyya andG.York \u201cAppearance Label Balanced Triplet Loss for Multi\u2010Modal Aerial View Object Classification \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2023) 19209\u201319218.","DOI":"10.1109\/CVPRW59228.2023.00060"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3205327"},{"key":"e_1_2_11_21_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3324706","article-title":"Long\u2010Range Correlation Supervision for Land\u2010Cover Classification From Remote Sensing Images","volume":"61","author":"Yu D.","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"1","key":"e_1_2_11_22_1","first-page":"128","article-title":"DPPNet: An Efficient and Robust Deep Learning Network for Land Cover Segmentation From High\u2010Resolution Satellite Images","volume":"7","author":"Sravya N.","year":"2022","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.5775\/fg.2016.137.i"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs15092263"}],"container-title":["IET Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70139","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/ipr2.70139","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:59:36Z","timestamp":1778284776000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/ipr2.70139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/ipr2.70139"],"URL":"https:\/\/doi.org\/10.1049\/ipr2.70139","archive":["Portico"],"relation":{},"ISSN":["1751-9659","1751-9667"],"issn-type":[{"value":"1751-9659","type":"print"},{"value":"1751-9667","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-12-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70139"}}