{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T06:45:43Z","timestamp":1785998743959,"version":"3.56.0"},"reference-count":37,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T00:00:00Z","timestamp":1630022400000},"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":["41801310"],"award-info":[{"award-number":["41801310"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Technology Development Plan Project of Henan Province","award":["202102210160, 202102110121"],"award-info":[{"award-number":["202102210160, 202102110121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural network (CNN) has shown excellent performance in hyperspectral image (HSI) classification. However, the structure of the CNN models is complex, requiring many training parameters and floating-point operations (FLOPs). This is often inefficient and results in longer training and testing time. In addition, the label samples of hyperspectral data are limited, and a deep network often causes the over-fitting phenomenon. Hence, a dual-path small convolution (DPSC) module is proposed. It is composed of two 1 \u00d7 1 small convolutions with a residual path and a density path. It can effectively extract abstract features from HSI. A dual-path small convolution network (DPSCN) is constructed by stacking DPSC modules. Specifically, the proposed model uses a DPSC module to complete the extraction of spectral and spectral\u2013spatial features successively. It then uses a global average pooling layer at the end of the model to replace the conventional fully connected layer to complete the final classification. In the implemented study, all convolutional layers of the proposed network, except the middle layer, use 1 \u00d7 1 small convolution, effectively reduced model parameters and increased the speed of feature extraction processes. DPSCN was compared with several current state-of-the-art models. The results on three benchmark HSI data sets demonstrated that the proposed model is of lower complexity, has stronger generalization ability, and has higher classification efficiency.<\/jats:p>","DOI":"10.3390\/rs13173411","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T21:59:45Z","timestamp":1630447185000},"page":"3411","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Dual-Path Small Convolution Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1053-6741","authenticated-orcid":false,"given":"Lanxue","family":"Dang","sequence":"first","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Laboratory of Spatial Information Processing, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peidong","family":"Pang","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianyu","family":"Zuo","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Laboratory of Spatial Information Processing, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7018-646X","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Laboratory of Spatial Information Processing, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8130-182X","authenticated-orcid":false,"given":"Jay","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Environment and Planning, Henan University, Kaifeng 475004, China"},{"name":"Department of Geography, Kent State University, Kent, OH 44240, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1109\/TGRS.2012.2198654","article-title":"Semisupervised Learning of Hyperspectral Data With Unknown Land-Cover Classes","volume":"51","author":"Jun","year":"2013","journal-title":"IEEE Trans. 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