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

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.

Paper Details

Date Published: 7 October 2019
PDF: 10 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111550V (7 October 2019); doi: 10.1117/12.2534459
Show Author Affiliations
Hadis Madani, Western Univ. (Canada)
Kenneth McIsaac, Western Univ. (Canada)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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