{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:19:33Z","timestamp":1760149173210,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"],"award-info":[{"award-number":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"]}]},{"name":"Natural Science Foundation of Chongqing","award":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"],"award-info":[{"award-number":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"]}]},{"name":"Key Scientific and Technological Innovation Project","award":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"],"award-info":[{"award-number":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"]}]},{"name":"Macao Young Scholars Program","award":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"],"award-info":[{"award-number":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"]}]},{"name":"Fundamental Research Funds","award":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"],"award-info":[{"award-number":["62201109","62071340","42201371","CSTB2022NSCQMSX0452","KJCXZD2020025","AM2020008","2023CDJXY-039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Representation learning-based hyperspectral target detection (HTD) methods generally follow a learning paradigm of single-layer or one-step representation residual learning and the target detection on original full spectral bands, which, in some cases, cannot accurately distinguish the target pixels from variable background pixels via one-round of the detection process. To alleviate the problem and make full use of the latent discriminate characteristics in different spectral bands and the representation residual, this paper proposes a level-wise band-partition-based hierarchical representation residual feature (LBHRF) learning method for HTD with a parallel and cascaded hybrid structure. Specifically, the LBHRR method proposes to partition and fuse different levels of sub-band spectra combinations, and take full advantages of the discriminate information in representation residuals from different levels of band-partition. The highlights of this work include three aspects. First, the original full spectral bands are partitioned in a parallel level-wise manner to obtain the augmented representation residual feature through level-wise band-partition-based representation residual learning, such that the global spectral integrity and contextual information of local adjacent sub-bands are flexibly fused. Second, the SoftMax transformation, pooling operation, and augmented representation residual feature reuse among different layers are equipped in cascade to enhance the learning ability of the nonlinear and discriminant hierarchical representation residual features of the method. Third, a hierarchical representation residual feature-based HTD method is developed in an efficient stepwise learning manner instead of back-propagation optimization. Experimental results on several HSI datasets demonstrate that the proposed model can yield promising detection performance in comparison to some state-of-the-art counterparts.<\/jats:p>","DOI":"10.3390\/rs15143608","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T21:21:46Z","timestamp":1689801706000},"page":"3608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Rethinking Representation Learning-Based Hyperspectral Target Detection: A Hierarchical Representation Residual Feature-Based Method"],"prefix":"10.3390","volume":"15","author":[{"given":"Tan","family":"Guo","sequence":"first","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7696-0775","authenticated-orcid":false,"given":"Fulin","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}]},{"given":"Yule","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Henan University of Technology, Zhengzhou 453000, China"}]},{"given":"Xinjian","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Guangyao","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"ref_1","first-page":"5503914","article-title":"CyCU-Net: Cycle-consistency unmixing network by learning cascaded autoencoders","volume":"60","author":"Gao","year":"2022","journal-title":"IEEE Trans. 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