{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:35:21Z","timestamp":1774528521192,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,24]],"date-time":"2021-07-24T00:00:00Z","timestamp":1627084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. To extract crop types from the airborne hyperspectral images, we propose a fine classification method based on multi-feature fusion and deep learning. In this research, the morphological profiles, GLCM texture and endmember abundance features are leveraged to exploit the spatial information of the hyperspectral imagery. Then, the multiple spatial information is fused with the original spectral information to generate classification result by using the deep neural network with conditional random field (DNN+CRF) model. Specifically, the deep neural network (DNN) is a deep recognition model which can extract depth features and mine the potential information of data. As a discriminant model, conditional random field (CRF) considers both spatial and contextual information to reduce the misclassification noises while keeping the object boundaries. Moreover, three multiple feature fusion approaches, namely feature stacking, decision fusion and probability fusion, are taken into account. In the experiments, two airborne hyperspectral remote sensing datasets (Honghu dataset and Xiong\u2019an dataset) are used. The experimental results show that the classification performance of the proposed method is satisfactory, where the salt and pepper noise is decreased, and the boundary of the ground object is preserved.<\/jats:p>","DOI":"10.3390\/rs13152917","type":"journal-article","created":{"date-parts":[[2021,7,25]],"date-time":"2021-07-25T22:07:00Z","timestamp":1627250820000},"page":"2917","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Lifei","family":"Wei","sequence":"first","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518034, China"}]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Qikai","family":"Lu","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Yajing","family":"Liang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Haibo","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Zhengxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Run","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2945-2708","authenticated-orcid":false,"given":"Liqin","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Printing and Packaging, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,24]]},"reference":[{"key":"ref_1","first-page":"68","article-title":"Crop Classification Using MODIS NDVI Data Denoised by Wavelet: A Case Study in Hebei Plain, China","volume":"3","author":"Zhang","year":"2011","journal-title":"Chin. 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