Next Article in Journal
Editorial for Special Issue “Hyperspectral Imaging and Applications”
Previous Article in Journal
Evaluating the Drought-Monitoring Utility of Four Satellite-Based Quantitative Precipitation Estimation Products at Global Scale
Previous Article in Special Issue
Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
Article

Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery

1
Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
2
Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 2011; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11172011
Received: 21 June 2019 / Revised: 20 August 2019 / Accepted: 23 August 2019 / Published: 27 August 2019
The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a precise crop classification method using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for UAV-borne hyperspectral remote sensing imagery is proposed in this paper. The proposed method integrates the spectral information, the spatial context, the spatial features, and the spatial location information in the conditional random field model by the probabilistic potentials, providing complementary information for the crop discrimination from different perspectives. The experimental results obtained with two UAV-borne high spatial resolution hyperspectral images confirm that the proposed method can solve the problems of large-scale spatial information modeling and spectral variability, improving the classification accuracy for each crop type. This method has important significance for the precise classification of crops in hyperspectral remote sensing imagery. View Full-Text
Keywords: hyperspectral remote sensing imagery; conditional random fields; spatial features; spatial location; precise crop classification; unmanned aerial vehicle hyperspectral remote sensing imagery; conditional random fields; spatial features; spatial location; precise crop classification; unmanned aerial vehicle
Show Figures

Graphical abstract

MDPI and ACS Style

Wei, L.; Yu, M.; Liang, Y.; Yuan, Z.; Huang, C.; Li, R.; Yu, Y. Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery. Remote Sens. 2019, 11, 2011. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11172011

AMA Style

Wei L, Yu M, Liang Y, Yuan Z, Huang C, Li R, Yu Y. Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery. Remote Sensing. 2019; 11(17):2011. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11172011

Chicago/Turabian Style

Wei, Lifei, Ming Yu, Yajing Liang, Ziran Yuan, Can Huang, Rong Li, and Yiwei Yu. 2019. "Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery" Remote Sensing 11, no. 17: 2011. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11172011

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop