{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:25:40Z","timestamp":1764937540156,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"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":["61971244","ZR2020MF011"],"award-info":[{"award-number":["61971244","ZR2020MF011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Natural Science Foundation","award":["61971244","ZR2020MF011"],"award-info":[{"award-number":["61971244","ZR2020MF011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the development of remote sensing technology, classification has become a meaningful way to explore the rich information in hyperspectral images (HSIs). However, various environmental factors may cause noise and shadow areas in HSIs, resulting in weak signals and difficulties in fully utilizing information. In addition, classification methods based on deep learning have made considerable progress, but features extracted from most networks have much redundancy. Therefore, a method based on two-dimensional dynamic stochastic resonance (2D DSR) shadow enhancement and convolutional neural network (CNN) classification combined with an attention mechanism (AM) for HSIs is proposed in this paper. Firstly, to protect the spatial correlation of HSIs, an iterative equation of 2D DSR based on the pixel neighborhood relationship was derived, which made it possible to perform matrix SR in the spatial dimension of the image, instead of one-dimensional vector resonance. Secondly, by using the noise in the shadow area to generate resonance, 2D DSR can help increase the signals in the shadow regions by preserving the spatial characteristics, and enhanced HSIs can be obtained. Then, a 3DCNN embedded with two efficient channel attention (ECA) modules and one convolutional block attention module (CBAM) was designed to make the most of critical features that significantly affect the classification accuracy by giving different weights. Finally, the performance of the proposed method was evaluated on a real-world HSI, and comparative studies were carried out. The experimental results showed that the proposed approach has promising prospects in HSIs\u2019 shadow enhancement and information mining.<\/jats:p>","DOI":"10.3390\/rs15071820","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T01:05:26Z","timestamp":1680138326000},"page":"1820","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Shadow Enhancement Using 2D Dynamic Stochastic Resonance for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Qiuyue","family":"Liu","sequence":"first","affiliation":[{"name":"College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"},{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"}]},{"given":"Min","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}]},{"given":"Xuefeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"},{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/JSTARS.2021.3133021","article-title":"Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects","volume":"15","author":"Ahmad","year":"2022","journal-title":"IEEE J. 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