Abstract
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.
Highlights
China is one of the most populous countries in the world
In this paper, in view of the problems of local over-smoothing and spectral variability faced by the CRF model in the precise classification of crops based on high-resolution hyperspectral remote sensing images, we propose a method for the precise classification of crops using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for unmanned aerial vehicle (UAV)-borne hyperspectral remote sensing imagery
Since the SSLF-CRF method combines spatial context, spatial features, spatial location information, and spectral information, the accuracy is improved by about 10%, which indicates the importance of spatial information for improving the classification accuracy
Summary
With the rapid development of urbanization, crop protection in China is becoming more and more important [1,2]. The traditional methods of obtaining crop planting information include field surveys and statistical sampling. These methods are both accurate and objective, they have shortcomings in large-scale implementation and do not provide accurate spatial distribution information for crop areas [4]. With the development of remote sensing technology, the use of remote sensing imagery to classify crops is an effective way to monitor the spatial distribution of agriculture and obtain basic data for crop growth monitoring and yield forecasting [5,6]
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