{"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/a-miniaturized-semantic-segmentation-method","title":"A Miniaturized Semantic Segmentation Method for Remote Sensing Image","arxiv_id":"1810.11603","date":"2018-10-27","proceeding":null,"authors":["Shou-Yu Chen","Guang-Sheng Chen","Wei-Peng Jing"],"abstract":"In order to save the memory, we propose a miniaturization method for neural\nnetwork to reduce the parameter quantity existed in remote sensing (RS) image\nsemantic segmentation model. The compact convolution optimization method is\nfirst used for standard U-Net to reduce the weights quantity. With the purpose\nof decreasing model performance loss caused by miniaturization and based on the\ncharacteristics of remote sensing image, fewer down-samplings and improved\ncascade atrous convolution are then used to improve the performance of the\nminiaturized U-Net. Compared with U-Net, our proposed Micro-Net not only\nachieves 29.26 times model compression, but also basically maintains the\nperformance unchanged on the public dataset. We provide a Keras and Tensorflow\nhybrid programming implementation for our model:\nhttps://github.com/Isnot2bad/Micro-Net","url_abs":"http://arxiv.org/abs/1810.11603v1","url_pdf":"http://arxiv.org/pdf/1810.11603v1.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":"a-miniaturized-semantic-segmentation-method","repo_url":"https://github.com/Isnot2bad/Micro-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"model-compression","task_name":"Model Compression"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}