{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T06:41:46Z","timestamp":1782456106957,"version":"3.54.5"},"reference-count":87,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"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>The research focus in remote sensing scene image classification has been recently shifting towards deep learning (DL) techniques. However, even the state-of-the-art deep-learning-based models have shown limited performance due to the inter-class similarity and the intra-class diversity among scene categories. To alleviate this issue, we propose to explore the spatial dependencies between different image regions and introduce patch-based discriminative learning (PBDL) for remote sensing scene classification. In particular, the proposed method employs multi-level feature learning based on small, medium, and large neighborhood regions to enhance the discriminative power of image representation. To achieve this, image patches are selected through a fixed-size sliding window, and sampling redundancy, a novel concept, is developed to minimize the occurrence of redundant features while sustaining the relevant features for the model. Apart from multi-level learning, we explicitly impose image pyramids to magnify the visual information of the scene images and optimize their positions and scale parameters locally. Motivated by this, a local descriptor is exploited to extract multi-level and multi-scale features that we represent in terms of a codeword histogram by performing k-means clustering. Finally, a simple fusion strategy is proposed to balance the contribution of individual features where the fused features are incorporated into a bidirectional long short-term memory (BiLSTM) network. Experimental results on the NWPU-RESISC45, AID, UC-Merced, and WHU-RS datasets demonstrate that the proposed approach yields significantly higher classification performance in comparison with existing state-of-the-art deep-learning-based methods.<\/jats:p>","DOI":"10.3390\/rs14235913","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T03:15:24Z","timestamp":1669173324000},"page":"5913","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Patch-Based Discriminative Learning for Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7191-0245","authenticated-orcid":false,"given":"Usman","family":"Muhammad","sequence":"first","affiliation":[{"name":"Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, FIN-90014 Oulu, Finland"},{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100864, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3154-2561","authenticated-orcid":false,"given":"Md Ziaul","family":"Hoque","sequence":"additional","affiliation":[{"name":"Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, FIN-90014 Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100864, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4422-8723","authenticated-orcid":false,"given":"Mourad","family":"Oussalah","sequence":"additional","affiliation":[{"name":"Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, FIN-90014 Oulu, Finland"},{"name":"Medical Imaging, Physics, and Technology (MIPT), Faculty of Medicine, University of Oulu, FIN-90014 Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.proenv.2016.03.029","article-title":"Mapping land cover using remote sensing data and GIS techniques: A case study of Prahova Subcarpathians","volume":"32","author":"Mihai","year":"2016","journal-title":"Procedia Environ. 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