NDLI: Spectral-spatial classification of hyperspectral imagery based on Random Forests

Content Provider	ACM Digital Library
Author	Su, Song-zhi Li, Shaozi Wei, Liu Zhang, Miaohui Ji, Rongrong Wu, Yundong
Abstract	The high dimensionality of hyperspectral images are usually coupled with limited reference data available, which degenerates the performances of supervised classification techniques such as random forests (RF). The commonly used pixel-wise classification lacks information about spatial structures of the image. In order to improve the performances of classification, incorporation of spectral and spatial is needed. This paper proposes a novel scheme for accurate spectral-spatial classification of hyperspectral image. It is based on random forests, followed by majority voting within the superpixels obtained by oversegmentation through a graph-based technique. The scheme combines the result of a pixel-wise RF classification and the segmentation map obtained by oversegmentation. Our experimental results on two hyperspectral images show that the proposed framework combining spectral information with spatial context can greatly improve the final result with respect to pixel-wise classification with Random Forests.
Starting Page	163
Ending Page	168
Page Count	6
File Format	PDF
ISBN	9781450322522
DOI	10.1145/2499788.2499853
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-08-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Hyperspectral Classification Random forests
Content Type	Text
Resource Type	Article

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Spectral-spatial classification of hyperspectral imagery based on Random Forests