Journal
REMOTE SENSING
Volume 11, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/rs11070780
Keywords
hyperspectral remote sensing imagery; conditional random fields; spectral-spatial fusion; fine crop classification; unmanned aerial vehicle
Categories
Funding
- National Key Research and Development Program of China [2017YFB0504202]
- National Natural Science Foundation of China [41622107]
- Special projects for technological innovation in Hubei [2018ABA078]
- Open fund of Key Laboratory of Ministry of education for spatial data mining and information sharing [2018LSDMIS05]
- Open fund of Key Laboratory of agricultural remote sensing of Ministry of Agriculture [20170007]
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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.
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