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RSS FeedsRemote Sensing, Vol. 11, Pages 1983: Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine (Remote Sensing)

 
 

22 august 2019 19:01:30

 
Remote Sensing, Vol. 11, Pages 1983: Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine (Remote Sensing)
 


Spectral-spatial classification of hyperspectral images (HSIs) has recently attracted great attention in the research domain of remote sensing. It is well-known that, in remote sensing applications, spectral features are the fundamental information and spatial patterns provide the complementary information. With both spectral features and spatial patterns, hyperspectral image (HSI) applications can be fully explored and the classification performance can be greatly improved. In reality, spatial patterns can be extracted to represent a line, a clustering of points or image texture, which denote the local or global spatial characteristic of HSIs. In this paper, we propose a spectral-spatial HSI classification model based on superpixel pattern (SP) and kernel based extreme learning machine (KELM), called SP-KELM, to identify the land covers of pixels in HSIs. In the proposed SP-KELM model, superpixel pattern features are extracted by an advanced principal component analysis (PCA), which is based on superpixel segmentation in HSIs and used to denote spatial information. The KELM method is then employed to be a classifier in the proposed spectral-spatial model with both the original spectral features and the extracted spatial pattern features. Experimental results on three publicly available HSI datasets verify the effectiveness of the proposed SP-KELM model, with the performance improvement of 10% over the spectral approaches.


 
241 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 1984: Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1978: Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images (Remote Sensing)
 
 
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