Oil slick extraction from hyperspectral images using a modified stacked auto-encoder network

2019 ◽  
Author(s):  
Wen CHANG ◽  
Bingxin Liu ◽  
Qiang ZHANG
Keyword(s):  
Hyperspectral Images ◽  
Oil Slick

Spatial–Spectral Jointed Stacked Auto-Encoder-Based Deep Learning for Oil Slick Extraction from Hyperspectral Images

2019 ◽  
Vol 47 (12) ◽  
pp. 1989-1997 ◽  
Author(s):  
Bingxin Liu ◽  
Qiang Zhang ◽  
Ying LI ◽  
Wen Chang ◽  
Manrui Zhou
Keyword(s):  
Deep Learning ◽  
Hyperspectral Images ◽  
Oil Slick

Pre-Processing of Hyperspectral Images Using Nonlinear Filters

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

Multi-temporal Hyperspectral Images Unmixing and Classification Based on 3D Signature Model and Matching

PIERS Online ◽  
2010 ◽  
Vol 6 (5) ◽  
pp. 480-484 ◽  
Author(s):  
Imed Riadh Farah ◽  
Selim Hemissi ◽  
Karim Saheb Ettabaa ◽  
Bassel Souleiman
Keyword(s):  
Hyperspectral Images ◽  
Multi Temporal

Compression of Hyperspectral Images by Using DWT and SPIHT

2017 ◽  
Vol 4 (1) ◽  
pp. 30
Author(s):  
VEERA NAVENDRA REDDY K ◽  
SIVAPPAGARI CHANDRA MOHAN REDDY ◽  
◽  
Keyword(s):  
Hyperspectral Images

Classification of hyperspectral images based on multifractal features and support vector machine

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Images ◽  
Support Vector
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