{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/solar-power-plant-detection-on-multi-spectral","title":"Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion","arxiv_id":"1704.06410","date":"2017-04-21","proceeding":null,"authors":["Nevrez Imamoglu","Motoki Kimura","Hiroki Miyamoto","Aito Fujita","Ryosuke Nakamura"],"abstract":"Most of the traditional convolutional neural networks (CNNs) implements\nbottom-up approach (feed-forward) for image classifications. However, many\nscientific studies demonstrate that visual perception in primates rely on both\nbottom-up and top-down connections. Therefore, in this work, we propose a CNN\nnetwork with feedback structure for Solar power plant detection on\nmiddle-resolution satellite images. To express the strength of the top-down\nconnections, we introduce feedback CNN network (FB-Net) to a baseline CNN model\nused for solar power plant classification on multi-spectral satellite data.\nMoreover, we introduce a method to improve class activation mapping (CAM) to\nour FB-Net, which takes advantage of multi-channel pulse coupled neural network\n(m-PCNN) for weakly-supervised localization of the solar power plants from the\nfeatures of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN,\nexperimental results demonstrated promising results on both solar-power plant\nimage classification and detection task.","url_abs":"http://arxiv.org/abs/1704.06410v2","url_pdf":"http://arxiv.org/pdf/1704.06410v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"solar-power-plant-detection-on-multi-spectral","repo_url":"https://github.com/gistairc/MUSIC4P3","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"cam","method_name":"CAM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}