Prof. Kenneth McIsaac Profile

Prof. Kenneth McIsaac

at Western Univ

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Proceedings Article | 7 October 2019 Paper

Spectral perturbation method for deep learning-based classification of remote sensing hyperspectral images

Hadis Madani, Kenneth McIsaac

Proceedings Volume 11155, 111550V (2019) https://doi.org/10.1117/12.2534459

KEYWORDS: Remote sensing, Hyperspectral imaging, Image classification, Data modeling, Neural networks, Feature extraction

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Deep learning-based hyperspectral image classification has become very popular recently. It has been widely used in the domain of classification of remote sensing hyperspectral images and has shown encouraging results. Although presence of many spectral bands in a hyperspectral image provides valuable features for the classification purposes, lack of adequate training samples makes training a deep learning model a challenging task. In this paper, we employed stacked auto-encoder (SAE) as the deep learning architecture to extract deep spectral features of the input data and to address the problem of absence of enough training samples, we proposed an effective data augmentation approach to boost the number of training data. Also, in order to alleviate the noise in the output image, we used the majority voting strategy to smooth the final classification map. We applied our method on the Indian Pines hyperspectral dataset including very few training examples. Experimental results show the superiority of our method comparing to some of the state of the arts algorithms.

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