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In this study, we focus on UAV-based forest fire imagery detection using a lightweight convolution neural network (CNN). The task is challenging owing to complex image backgrounds and insufficient training samples. Specifically, we enhance the MobileNetV2 model with an attention mechanism for UAV-based image classification. The proposed model first employs a transfer learning strategy that leverages the pre-trained weights from ImageNet to expedite learning. Then, the model incorporates randomly initialised weights and dropout mechanisms to mitigate over-fitting during training. In addition, an ensemble framework with a majority voting scheme is adopted to improve the classification performance. A case study on forest fire scenes classification with benchmark and real-world images is demonstrated. The results on a publicly available UAV-based image data set reveal the competitiveness of our proposed model as compared with those from existing methods. In addition, based on a set of self-collected images with complex backgrounds, the proposed model illustrates its generalisation capability to undertake forest fire classification tasks with aerial images.<\/jats:p>","DOI":"10.1007\/s00500-025-10512-3","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T16:11:31Z","timestamp":1740759091000},"page":"2363-2378","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A lightweight CNN model for UAV-based image classification"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9145-2771","authenticated-orcid":false,"given":"Xinjie","family":"Deng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Burhan","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yit Hong","family":"Choo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fazal","family":"Ghaffar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chee Peng","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"issue":"1","key":"10512_CR1","doi-asserted-by":"publisher","first-page":"228","DOI":"10.3390\/electronics12010228","volume":"12","author":"C Bahhar","year":"2023","unstructured":"Bahhar C, Ksibi A, Ayadi M, Jamjoom MM, Ullah Z, Soufiene BO, Sakli H (2023) Wildfire and smoke detection using staged yolo model and ensemble cnn. 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Retrieved from https:\/\/arxiv.org\/abs\/1707.07012","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10512-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10512-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10512-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T06:37:10Z","timestamp":1742193430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-025-10512-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":63,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10512"],"URL":"https:\/\/doi.org\/10.1007\/s00500-025-10512-3","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"8 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Open Access funding enabled and organized by CAUL and its Member Institutions. This work was supported by the Natural Science Foundation of Chongqing Science and Technology Commission (Grant No. CSTB2023NSCQ-MSX0478) and the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202304315 and KJZD-K202404303).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}