{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T09:09:36Z","timestamp":1721812176613},"reference-count":28,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:00:00Z","timestamp":1625184000000},"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":["No. 61672405, No. 62077038"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Shaanxi Province of China","award":["No. 2021JM-459, No. 2018JM4018","No. 2021GY-280"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"To achieve effective deep fusion features for improving the classification accuracy of hyperspectral remote sensing images (HRSIs), a pixel frequency spectrum feature is presented and introduced to convolutional neural networks (CNNs). Firstly, the fast Fourier transform is performed on each spectral pixel to obtain the amplitude spectrum, i.e., the pixel frequency spectrum feature. Then, the obtained pixel frequency spectrum is combined with the spectral pixel to form a mixed feature, i.e., spectral and frequency spectrum mixed feature (SFMF). Several multi-branch CNNs fed with pixel frequency spectrum, SFMF, spectral pixel, and spatial features are designed for extracting deep fusion features. A pre-learning strategy, i.e., basic single branch CNNs are used to pre-learn the weights of a multi-branch CNN, is also presented for improving the network convergence speed and avoiding the network from getting into a locally optimal solution to a certain extent. And after reducing the dimensionality of SFMF by principal component analysis (PCA), a 3-dimensionality (3-D) CNN is also designed to further extract the joint spatial-SFMF feature. The experimental results of three real HRSIs show that adding the presented frequency spectrum feature into CNNs can achieve better recognition results, which in turn proves that the presented multi-branch CNNs can obtain the deep fusion features with more discriminant information.<\/jats:p>","DOI":"10.3390\/rs13132599","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T14:06:34Z","timestamp":1625234794000},"page":"2599","source":"Crossref","is-referenced-by-count":16,"title":["Hyperspectral Remote Sensing Images Deep Feature Extraction Based on Mixed Feature and Convolutional Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Jing","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Zhe","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9993-0731","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Caihong","family":"Mu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/JSTARS.2013.2267204","article-title":"Progress in Hyperspectral Remote Sensing Science and Technology in China over the Past Three Decades","volume":"7","author":"Tong","year":"2014","journal-title":"IEEE J. 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