{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T14:19:52Z","timestamp":1778163592384,"version":"3.51.4"},"reference-count":79,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D program of China","award":["2022YFB4300700"],"award-info":[{"award-number":["2022YFB4300700"]}]},{"name":"National Key R&amp;D program of China","award":["QCYRCXM-2022-21"],"award-info":[{"award-number":["QCYRCXM-2022-21"]}]},{"name":"National Key R&amp;D program of China","award":["2021B0909050008"],"award-info":[{"award-number":["2021B0909050008"]}]},{"name":"high-level innovative and entrepreneurial talent project","award":["2022YFB4300700"],"award-info":[{"award-number":["2022YFB4300700"]}]},{"name":"high-level innovative and entrepreneurial talent project","award":["QCYRCXM-2022-21"],"award-info":[{"award-number":["QCYRCXM-2022-21"]}]},{"name":"high-level innovative and entrepreneurial talent project","award":["2021B0909050008"],"award-info":[{"award-number":["2021B0909050008"]}]},{"name":"Guangdong High Level Innovation Research Institution Project","award":["2022YFB4300700"],"award-info":[{"award-number":["2022YFB4300700"]}]},{"name":"Guangdong High Level Innovation Research Institution Project","award":["QCYRCXM-2022-21"],"award-info":[{"award-number":["QCYRCXM-2022-21"]}]},{"name":"Guangdong High Level Innovation Research Institution Project","award":["2021B0909050008"],"award-info":[{"award-number":["2021B0909050008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The application of deep learning in remote sensing image classification has been paid more and more attention by industry and academia. However, manually designed remote sensing image classification models based on convolutional neural networks usually require sophisticated expert knowledge. Moreover, it is notoriously difficult to design a model with both high classification accuracy and few parameters. Recently, neural architecture search (NAS) has emerged as an effective method that can greatly reduce the heavy burden of manually designing models. However, it remains a challenge to search for a classification model with high classification accuracy and few parameters in the huge search space. To tackle this challenge, we propose TPENAS, a two-phase evolutionary neural architecture search framework, which optimizes the model using computational intelligence techniques in two search phases. In the first search phase, TPENAS searches for the optimal depth of the model. In the second search phase, TPENAS searches for the structure of the model from the perspective of the whole model. Experiments on three open benchmark datasets demonstrate that our proposed TPENAS outperforms the state-of-the-art baselines in both classification accuracy and reducing parameters.<\/jats:p>","DOI":"10.3390\/rs15082212","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T10:11:25Z","timestamp":1682071885000},"page":"2212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4526-2106","authenticated-orcid":false,"given":"Lei","family":"Ao","sequence":"first","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiyuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6602-7212","authenticated-orcid":false,"given":"Kai","family":"Sheng","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"He","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4754-7049","authenticated-orcid":false,"given":"Zigang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5521614","DOI":"10.1109\/TGRS.2021.3139077","article-title":"A spectral and spatial attention network for change detection in hyperspectral images","volume":"60","author":"Gong","year":"2022","journal-title":"IEEE Trans. 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