{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:49:03Z","timestamp":1761864543217,"version":"build-2065373602"},"reference-count":34,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>Image classification using polarimetric synthetic aperture radar (Pol-SAR) is becoming more important in image processing for remote sensing applications. However, in the existing techniques, during the feature extraction process, there exist some limitations including laborious endeavour for Pol-SAR image classification, identifying intrinsic features for target recognition is difficult in feature selection, and pixel-level Pol-SAR image classification is difficult for obtaining more precise and coherent interpretation consequences. Hence to overcome these issues, a novel Multifarious Stratification Stratagem in machine learning is proposed to achieve pixel-level Pol-SAR classification. In this proposed model, a novel Scrumptious Integrant Wrenching method is used for efficient feature extraction. It is compatible with the orientation-sensitive of the Pol-SAR image which increases the variety of intra-layer features. To remove the difficulty in feature selection, a novel Episodicical Proximity Selection method is proposed in which a Split-level parallel feature selection strategy is used to select the best qualities from the extracted features. To tackle the difficulty in classification, an Elastic Net Classifier (ENC) is used that find the coefficient vector for the linear combination of the training sets. This efficiently classified the best features in the Pol-SAR images and improved the proposed system\u2019s accuracy. As a result, the performance measures of the proposed system demonstrate that the accuracy is increased by 99.69%, precision is increased by 98.99%, recall is increased by 98.99%, sensitivity is increased by 98.99%, and F1-score is increased by 98.99% as a response.<\/jats:p>","DOI":"10.3233\/jifs-222403","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T15:47:14Z","timestamp":1712332034000},"page":"411-430","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Pol-SAR image classification using multifarious stratification stratagem in machine learning"],"prefix":"10.1177","volume":"48","author":[{"given":"P.V.","family":"Ashwin","sequence":"first","affiliation":[{"name":"Saintgits College of Engineering, APJ Abdul Kalam Kerala Technological University, Kerala, India"}]},{"given":"K.A.","family":"Ansal","sequence":"additional","affiliation":[{"name":"Saintgits College of Engineering, APJ Abdul Kalam Kerala Technological University, Kerala, India"}]}],"member":"179","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Ren Shijie Feng Zhou Semi-supervised Classification for PolSAR Data with Multi-scale Evolving Weighted Graph Convolutional Network IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2021).","DOI":"10.1109\/JSTARS.2021.3061418"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713946"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2641240"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2964679"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2645226"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.05.028"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Watanabe K. 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