Fast Chaotic Encryption for Hyperspectral Images

Processing and Analysis of Hyperspectral Data ◽

10.5772/intechopen.88980 ◽

2020 ◽

Author(s):

Carlos Villaseñor ◽

Javier Gomez-Avila ◽

Nancy Arana-Daniel ◽

Alma Y. Alanis ◽

Carlos Lopez-Franco

Keyword(s):

Hyperspectral Images ◽

Chaotic Encryption

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Parallel Crossed Chaotic Encryption for Hyperspectral Images

Applied Sciences ◽

10.3390/app8071183 ◽

2018 ◽

Vol 8 (7) ◽

pp. 1183 ◽

Cited By ~ 1

Author(s):

Carlos Villaseñor ◽

Eric Gutierrez-Frias ◽

Nancy Arana-Daniel ◽

Alma Alanis ◽

Carlos Lopez-Franco

Keyword(s):

Chaotic Systems ◽

Ad Hoc ◽

State Of The Art ◽

Hyperspectral Images ◽

Encryption Algorithm ◽

Electromagnetic Spectrum ◽

Chaotic Encryption ◽

Raw Data ◽

Essential Tool ◽

Encryption Schemes

Hyperspectral images (HI) collect information from across the electromagnetic spectrum, and they are an essential tool for identifying materials, recognizing processes and finding objects. However, the information on an HI could be sensitive and must to be protected. Although there are many encryption schemes for images and raw data, there are not specific schemes for HI. In this paper, we introduce the idea of crossed chaotic systems and we present an ad hoc parallel crossed chaotic encryption algorithm for HI, in which we take advantage of the multidimensionality nature of the HI. Consequently, we obtain a faster encryption algorithm and with a higher entropy result than others state of the art chaotic schemes.

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NEURAL NETWORK CLASSIFICATION OF HYPERSPECTRAL IMAGES ON THE BASIS OF THE HILBERT-HUANG TRANSFORM

Автометрия ◽

10.15372/aut20170209 ◽

2017 ◽

Keyword(s):

Neural Network ◽

Hyperspectral Images ◽

Hilbert Huang Transform ◽

Neural Network Classification

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Pre-Processing of Hyperspectral Images Using Nonlinear Filters

Information Technologies and Control ◽

10.2478/itc-2013-0002 ◽

2013 ◽

Vol 11 (1) ◽

pp. 8-13

Author(s):

V. Behar ◽

V. Bogdanova

Keyword(s):

Signal To Noise Ratio ◽

Correlation Coefficients ◽

Hyperspectral Images ◽

Spectral Correlation ◽

Edge Preserving ◽

Improvement Factor ◽

Filter Output ◽

Average Improvement ◽

Processing Procedure

Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6

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