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

Remote Sensing

卷:11

Rong Li¹  Yiwei Yu¹  Ming Yu¹  Can Huang¹  Yajing Liang¹  Lifei Wei¹  Ziran Yuan¹ 

[1] Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China;

关键词: hyperspectral remote sensing imagery;    conditional random fields;    spatial features;    spatial location;    precise crop classification;    unmanned aerial vehicle;   

DOI  :  10.3390/rs11172011

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown