{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:37:36Z","timestamp":1760233056214,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971101","62271116"],"award-info":[{"award-number":["61971101","62271116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, deep convolutional neural networks (DCNNs) have been widely used for object detection tasks in remote sensing images. However, the over-parametrization problem of DCNNs hinders their application in resource-constrained remote sensing devices. In order to solve this problem, we propose a network pruning method (named absorption pruning) to compress the remote sensing object detection network. Unlike the classical iterative three-stage pruning pipeline used in existing methods, absorption pruning is designed as a four-stage pruning pipeline that only needs to be executed once, which differentiates it from existing methods. Furthermore, the absorption pruning no longer identifies unimportant filters, as in existing pruning methods, but instead selects filters that are easy to learn. In addition, we design a method for pruning ratio adjustment based on the object characteristics in remote sensing images, which can help absorption pruning to better compress deep neural networks for remote sensing image processing. The experimental results on two typical remote sensing data sets\u2014SSDD and RSOD\u2014demonstrate that the absorption pruning method not only can remove 60% of the filter parameters from CenterNet101 harmlessly but also eliminate the over-fitting problem of the pre-trained network.<\/jats:p>","DOI":"10.3390\/rs14246245","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T06:55:03Z","timestamp":1670568903000},"page":"6245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Absorption Pruning of Deep Neural Network for Object Detection in Remote Sensing Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2882-7053","authenticated-orcid":false,"given":"Jielei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1155-786X","authenticated-orcid":false,"given":"Zongyong","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Zhipeng","family":"Zang","sequence":"additional","affiliation":[{"name":"Beijing Huahang Radio Measurement Research Institute, Beijing 102445, China"}]},{"given":"Xiangjie","family":"Meng","sequence":"additional","affiliation":[{"name":"Beijing Huahang Radio Measurement Research Institute, Beijing 102445, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0117-9087","authenticated-orcid":false,"given":"Zongjie","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Oriented Gaussian Function-Based Box Boundary-Aware Vectors for Oriented Ship Detection in Multiresolution SAR Imagery","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. 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