scholarly journals ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 780
Author(s):  
Na Li ◽  
Zhaopeng Xu ◽  
Huijie Zhao ◽  
Xinchen Huang ◽  
Zhenhong Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Imagery

Classification of Urban Hyperspectral Remote Sensing Imagery Based on Optimized Spectral Angle Mapping

2019 ◽  
Vol 47 (2) ◽  
pp. 289-294 ◽  
Author(s):  
Yu Liu ◽  
Shan Lu ◽  
Xingtong Lu ◽  
Zheyi Wang ◽  
Chun Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Imagery ◽  
Spectral Angle

Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery

2016 ◽  
Vol 54 (6) ◽  
pp. 3248-3264 ◽  
Author(s):  
Xian Guo ◽  
Xin Huang ◽  
Lefei Zhang ◽  
Liangpei Zhang ◽  
Antonio Plaza ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Imagery

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

2019 ◽  
Vol 11 (17) ◽  
pp. 2011 ◽  
Author(s):  
Lifei Wei ◽  
Ming Yu ◽  
Yajing Liang ◽  
Ziran Yuan ◽  
Can Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Fields ◽  
Large Scale ◽  
Conditional Random Fields ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Spatial Location ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Imagery

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.

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