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Open AccessArticle

Spatial–Spectral Fusion Based on Conditional Random Fields for the Fine Classification of Crops in UAV-Borne Hyperspectral Remote Sensing Imagery

1
Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
College of Computer Science, China University of Geosciences, Wuhan 430074, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 780; https://doi.org/10.3390/rs11070780
Received: 17 February 2019 / Revised: 21 March 2019 / Accepted: 28 March 2019 / Published: 1 April 2019
(This article belongs to the Special Issue Advanced Topics in Remote Sensing)
The fine classification of crops is critical for food security and agricultural management. There are many different species of crops, some of which have similar spectral curves. As a result, the precise classification of crops is a difficult task. Although the classification methods that incorporate spatial information can reduce the noise and improve the classification accuracy, to a certain extent, the problem is far from solved. Therefore, in this paper, the method of spatial–spectral fusion based on conditional random fields (SSF-CRF) for the fine classification of crops in UAV-borne hyperspectral remote sensing imagery is presented. The proposed method designs suitable potential functions in a pairwise conditional random field model, fusing the spectral and spatial features to reduce the spectral variation within the homogenous regions and accurately identify the crops. The experiments on hyperspectral datasets of the cities of Hanchuan and Honghu in China showed that, compared with the traditional methods, the proposed classification method can effectively improve the classification accuracy, protect the edges and shapes of the features, and relieve excessive smoothing, while retaining detailed information. This method has important significance for the fine classification of crops in hyperspectral remote sensing imagery. View Full-Text
Keywords: hyperspectral remote sensing imagery; conditional random fields; spectral–spatial fusion; fine crop classification; unmanned aerial vehicle hyperspectral remote sensing imagery; conditional random fields; spectral–spatial fusion; fine crop classification; unmanned aerial vehicle
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MDPI and ACS Style

Wei, L.; Yu, M.; Zhong, Y.; Zhao, J.; Liang, Y.; Hu, X. Spatial–Spectral Fusion Based on Conditional Random Fields for the Fine Classification of Crops in UAV-Borne Hyperspectral Remote Sensing Imagery. Remote Sens. 2019, 11, 780.

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