{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T12:40:04Z","timestamp":1766407204461,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundations of China","award":["61901369","62071387","62101454","61834005","61772417","20200203","2020AAA0104603","2021GY-029"],"award-info":[{"award-number":["61901369","62071387","62101454","61834005","61772417","20200203","2020AAA0104603","2021GY-029"]}]},{"name":"Foundation of National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology","award":["61901369","62071387","62101454","61834005","61772417","20200203","2020AAA0104603","2021GY-029"],"award-info":[{"award-number":["61901369","62071387","62101454","61834005","61772417","20200203","2020AAA0104603","2021GY-029"]}]},{"name":"National Key Research and Development Project of China","award":["61901369","62071387","62101454","61834005","61772417","20200203","2020AAA0104603","2021GY-029"],"award-info":[{"award-number":["61901369","62071387","62101454","61834005","61772417","20200203","2020AAA0104603","2021GY-029"]}]},{"name":"Shaanxi province key R&amp;D plan","award":["61901369","62071387","62101454","61834005","61772417","20200203","2020AAA0104603","2021GY-029"],"award-info":[{"award-number":["61901369","62071387","62101454","61834005","61772417","20200203","2020AAA0104603","2021GY-029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, hyperspectral image (HSI) classification (HSIC) methods that use deep learning have proved to be effective. In particular, the utilization of convolutional neural networks (CNNs) has proved to be highly effective. However, some key issues need to be addressed when classifying hyperspectral images (HSIs), such as small samples, which can influence the generalization ability of the CNNs and the HSIC results. To address this problem, we present a new network that integrates hybrid pyramid feature fusion and coordinate attention for enhancing small sample HSI classification results. The innovative nature of this paper lies in three main areas. Firstly, a baseline network is designed. This is a simple hybrid 3D-2D CNN. Using this baseline network, more robust spectral-spatial feature information can be obtained from the HSI. Secondly, a hybrid pyramid feature fusion mechanism is used, meaning that the feature maps of different levels and scales can be effectively fused to enhance the feature extracted by the model. Finally, coordinate attention mechanisms are utilized in the network, which can not only adaptively capture the information of the spectral dimension, but also include the direction-aware and position sensitive information. By doing this, the proposed CNN structure can extract more useful HSI features and effectively be generalized to test samples. The proposed method was shown to obtain better results than several existing methods by experimenting on three public HSI datasets.<\/jats:p>","DOI":"10.3390\/rs14102355","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"2355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Integrating Hybrid Pyramid Feature Fusion and Coordinate Attention for Effective Small Sample Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8101-5738","authenticated-orcid":false,"given":"Chen","family":"Ding","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"given":"Youfa","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3865-7588","authenticated-orcid":false,"given":"Runze","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"given":"Dushi","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"given":"Xiaoyan","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0655-056X","authenticated-orcid":false,"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi\u2019an 710129, China"}]},{"given":"Yanning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi\u2019an 710129, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/JSTARS.2021.3133021","article-title":"Hyperspectral Image Classification\u2014Traditional to Deep Models: A Survey for Future Prospects","volume":"15","author":"Ahmad","year":"2022","journal-title":"IEEE J. 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