Iterative Random Training Sampling Spectral Spatial Classification for Hyperspectral Images

Hyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecting training data. This is because a different set...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.3986-4007
Main Authors: Chang, Chein-I, Ma, Kenneth Yeonkong, Liang, Chia-Chen, Kuo, Yi-Mei, Chen, Shuhan, Zhong, Shengwei
Format: Article
Language:English
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Accuracy
Classification
Computer science
Confidence intervals
Cubes
Entropy
Feedback loops
Hyperspectral image classification (HSIC)
Hyperspectral imaging
Image classification
iterative random training sampling (IRTS)
random training sampling (RTS)
Sampling
Spatial data
Spatial filtering
spectral-spatial (SS)
Support vector machines
Training
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Description
Summary:Hyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecting training data. This is because a different set of training samples may produce a different classification result. A general approach to addressing this problem is the so-called K-fold method which implements RTS K times and takes the average of overall accuracy with respect to standard deviation to describe a confidence level of classification performance. To deal with this issue, this article develops an iterative RTS (IRTS) method as an alternative to the K-fold method to reduce the uncertainty caused by RTS. Its idea is to add the spatial filtered classification maps to the image cube that is currently being processed via feedback loops to augment image cubes iteratively. Then, the training samples will be reselected randomly from the new augmented image cubes iteration-by-iteration. As a result, the training samples selected from each iteration will be updated by new added spatial information captured by spatial filters implemented at the iteration. The experimental results clearly demonstrate that IRTS successfully improves classification accuracy as well as reduces inconsistency in results.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2020.3008359