{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T09:26:41Z","timestamp":1777109201128,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"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":["42101468 and 61701047"],"award-info":[{"award-number":["42101468 and 61701047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation of Hunan, Education Committee","award":["No.20B038"],"award-info":[{"award-number":["No.20B038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although deep learning has achieved great success in aircraft detection from SAR imagery, its blackbox behavior has been criticized for low comprehensibility and interpretability. Such challenges have impeded the trustworthiness and wide application of deep learning techniques in SAR image analytics. In this paper, we propose an innovative eXplainable Artificial Intelligence (XAI) framework to glassbox deep neural networks (DNN) by using aircraft detection as a case study. This framework is composed of three parts: hybrid global attribution mapping (HGAM) for backbone network selection, path aggregation network (PANet), and class-specific confidence scores mapping (CCSM) for visualization of the detector. HGAM integrates the local and global XAI techniques to evaluate the effectiveness of DNN feature extraction; PANet provides advanced feature fusion to generate multi-scale prediction feature maps; while CCSM relies on visualization methods to examine the detection performance with given DNN and input SAR images. This framework can select the optimal backbone DNN for aircraft detection and map the detection performance for better understanding of the DNN. We verify its effectiveness with experiments using Gaofen-3 imagery. Our XAI framework offers an explainable approach to design, develop, and deploy DNN for SAR image analytics.<\/jats:p>","DOI":"10.3390\/rs13183650","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T23:32:23Z","timestamp":1631575943000},"page":"3650","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Glassboxing Deep Learning to Enhance Aircraft Detection from SAR Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5665-0473","authenticated-orcid":false,"given":"Ru","family":"Luo","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science & Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5693-3414","authenticated-orcid":false,"given":"Jin","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2432-9583","authenticated-orcid":false,"given":"Lifu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science & Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhouhao","family":"Pan","sequence":"additional","affiliation":[{"name":"Research Department, China Academy of Electronics and Information Technology, Beijing 100041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingmin","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science & Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengqi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science & Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jielan","family":"Wang","sequence":"additional","affiliation":[{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science & Technology, Changsha 410014, China"},{"name":"School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8081-4239","authenticated-orcid":false,"given":"Alistair","family":"Ford","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6014","DOI":"10.1109\/ACCESS.2016.2611492","article-title":"Automatic target recognition in synthetic aperture radar im-agery: A state-of-the-art review","volume":"4","author":"Gill","year":"2016","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, J., Xiao, H., Chen, L., Xing, J., Pan, Z., Luo, R., and Cai, X. 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