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2020 | OriginalPaper | Chapter

Dual Convolutional Neural Networks for Hyperspectral Satellite Images Classification (DCNN-HSI)

Authors : Maissa Hamouda, Med Salim Bouhlel

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Hyperspectral Satellite Images (HSI) presents a very interesting technology for mapping, environmental protection, and security. HSI is very rich in spectral and spatial characteristics, which are non-linear and highly correlated which makes classification difficult. In this paper, we propose a new approach to the reduction and classification of HSI. This deep approach consisting of a dual Convolutional Neural Networks (DCNN), which aims to improve precision and computing time. This approach involves two main steps; the first is to extract the spectral data and reduce it by CNN until a single value representing the active pixel is displayed. The second consists in classifying the only remaining spatial band on CNN until the class of each pixel is obtained. The tests were applied to three different hyperspectral data sets and showed the effectiveness of the proposed method.

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previous chapter DRGCN: Deep Relation GCN for Group Activity Recognition

next chapter Edge Curve Estimation by the Nonparametric Parzen Kernel Method

Batch: Group of pixels containing the active pixel surrounded by its spatial neighbors.

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Title: Dual Convolutional Neural Networks for Hyperspectral Satellite Images Classification (DCNN-HSI)
Authors: Maissa Hamouda
Med Salim Bouhlel
Publisher: Springer International Publishing
Book: Neural Information Processing
Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-63820-7_42

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