{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T14:33:49Z","timestamp":1768142029600,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T00:00:00Z","timestamp":1615852800000},"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":["61703328"],"award-info":[{"award-number":["61703328"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M631165"],"award-info":[{"award-number":["2018M631165"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009996","name":"Shaanxi Province Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018BSHYDZZ23"],"award-info":[{"award-number":["2018BSHYDZZ23"]}],"id":[{"id":"10.13039\/501100009996","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XJJ2018254"],"award-info":[{"award-number":["XJJ2018254"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Small object detection in very-high-resolution (VHR) optical remote sensing images is a fundamental but challenaging problem due to the latent complexities. To tackle this problem, the MdrlEcf model is proposed by modifying deep reinforcement learning (DRL) and extracting the efficient convolution feature. Firstly, an efficient attention network is constructed by introducing the local attention into the convolutional neural network. Combining the shallow low-level features with rich detail descriptions and high-level features with more semantic meanings effectively, efficient convolution features can be obtained. By this, the attention network can effectively enhance the ability to extract small target features and suppressing useless features. Secondly, the efficient feature map is sent to the region proposal network constructed by modified DRL. Using the modified reward function, this model can accumulate more rewards to conduct the search process, and potentially generate effective subsequent proposals and classification scores. It also can increase the effectiveness of object locations and classifications for small targets. Quantitative and qualitative experiments are conducted to verify the detection performance of different models. The results show that the proposed MdrlEcf can effectively and accurately locate and identify related small objects.<\/jats:p>","DOI":"10.3390\/ijgi10030170","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T12:01:02Z","timestamp":1615896062000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Modified Deep Reinforcement Learning with Efficient Convolution Feature for Small Target Detection in VHR Remote Sensing Imagery"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0327-6729","authenticated-orcid":false,"given":"Shuai","family":"Liu","sequence":"first","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialan","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100301","DOI":"10.1016\/j.cosrev.2020.100301","article-title":"A comprehensive and systematic look up into deep learning based object detection techniques: A review","volume":"38","author":"Sharma","year":"2020","journal-title":"Comput. 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