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Processing of Hyperspectral Data using Wavelet Transform
2018
FUOYE Journal of Engineering and Technology
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Therefore, this research work is out to make use of Wavelet Transform for processing signals obtained from hyperspectral images with a view to denoise and reduce the data dimensionality without losing ...
Remote sensor technology has encouraged series of research work in the area of signal and image processing. ...
In this work, each band of hyperspectral remote sensing image is transformed into the curvelet domain and the sets of the sub band images are obtained from different wavelength of hyperspectral remote ...
doi:10.46792/fuoyejet.v3i1.144
fatcat:fcveglqbdbd2na7v57gajfu4o4
HYPERSPECTRAL IMAGE DENOISING WITH CUBIC TOTAL VARIATION MODEL
2012
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Image noise is generated unavoidably in the hyperspectral image acquision process and has a negative effect on subsequent image analysis. ...
The augmented Lagrangian method is utilized to improve the speed of solution of the desired hyperspectral image. ...
[6] proposed a new hyperspectral image denoising algorithm by
adding a PCA transform before using wavelet shrinkage; first, a
However, most of these denoising algorithms deal with
hyperspectral ...
doi:10.5194/isprsannals-i-7-95-2012
fatcat:ldjv2rbvxnhzji33kiaqwpq45i
OIL SPILL AISA+ HYPERSPECTRAL DATA DETECTION BASED ON DIFFERENT SEA SURFACE GLINT SUPPRESSION METHODS
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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This paper takes AISA+ airborne hyperspectral oil spill image as data source, using multi-scale wavelet transform, enhanced Lee filter, enhanced Frost filter and mean filter method for sea surface glint ...
to extract oil spill feature information from the remote sensing data. ...
I'd like to express my sincere appreciation to China Marine Surveillance North Sea Aviation detachment for their efforts in providing the AISA + airborne hyperspectral oil spill image. ...
doi:10.5194/isprs-archives-xlii-3-2083-2018
fatcat:6n5k3y2arbev7bcbefhoyz5sme
HYPERSPECTRAL IMAGE DENOISING USING A NONLOCAL SPECTRAL SPATIAL PRINCIPAL COMPONENT ANALYSIS
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification ...
In this paper, we develop a noise reduction method based on principal component analysis (PCA) for hyperspectral imagery, which is dependent on the assumption that the noise can be removed by selecting ...
ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China Grant 41501410 and the State Grid Corporation Science and Technology Project of GCB17201700121. ...
doi:10.5194/isprs-archives-xlii-3-789-2018
fatcat:cilx5d7lujby7atnuldfya73lq
Analysis and Denoising of Hyperspectral Remote Sensing Image in the Curvelet Domain
2013
Mathematical Problems in Engineering
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A new denoising algorithm is proposed according to the characteristics of hyperspectral remote sensing image (HRSI) in the curvelet domain. ...
Firstly, each band of HRSI is transformed into the curvelet domain, and the sets of subband images are obtained from different wavelength of HRSI. ...
Acknowledgment This work was supported by the National Natural Science Foundation of China under Project 61101183 and Project 41201363. ...
doi:10.1155/2013/751716
fatcat:vnjypql4lvew5nexm2cjifvxce
Junk band recovery for hyperspectral image based on curvelet transform
2011
Journal of Central South University of Technology
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The performance of this method is tested on the hyperspectral data cube obtained by airborne visible/infrared imaging spectrometer (AVIRIS). ...
Under consideration that the profiles of bands at close wavelengths are quite similar and the curvelets are good at capturing profiles, a junk band recovery algorithm for hyperspectral data based on curvelet ...
based on wavelet; (f) This method 5 Conclusions 1) A hyperspectral image junk band recovery method is proposed based on curvelet transform and the significant spectral correlation. ...
doi:10.1007/s11771-011-0767-6
fatcat:sdzopnvngzg4za7hw6xrevbz2m
Wavelet shrinkage denoising of intrinsic mode functions of hyperspectral image bands for classification with high accuracy
2009
2009 IEEE International Geoscience and Remote Sensing Symposium
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This paper proposes Empirical Mode Decomposition (EMD) followed by wavelet shrinkage denoising in hyperspectral image classification to improve classification accuracy. ...
In this paper, firstly, EMD is applied to each hyperspectral image band separately to obtain the IMFs of all image bands. ...
Different wavelet types can be used for the wavelet transform and the performance changes based on the wavelet type, while EMD does not use any basis functions and decomposes the signal according to intrinsic ...
doi:10.1109/igarss.2009.5417940
dblp:conf/igarss/DemirEG09
fatcat:lg6h3umtu5akvl3uauevu5hvtu
HYPERSPECTRAL IMAGE DENOISING USING MULTIPLE LINEAR REGRESSION AND BIVARIATE SHRINKAGE WITH 2-D DUAL-TREE COMPLEX WAVELET IN THE SPECTRAL DERIVATIVE DOMAIN
2016
Boletim de Ciências Geodésicas
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In this paper, a new denoising method is proposed for hyperspectral remote sensing images, and tested on both the simulated and the real-life datacubes. ...
Predicted datacube of the hyperspectral images is calculated by multiple linear regression in the spectral domain based on the strong spectral correlation of the useful signal and the inter-band uncorrelation ...
AKCNOWLEDGEMENT This work is supported by the National Natural Science Foundation of China under Project 61101183 and Project 41201363. ...
doi:10.1590/s1982-21702016000400047
fatcat:j5h3mx4orvdjbd6kptbc52gqje
Denoising Hyperspectral Imagery and Recovering Junk Bands using Wavelets and Sparse Approximation
2006
2006 IEEE International Symposium on Geoscience and Remote Sensing
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Our experiments show that it outperforms wavelet-based global soft thresholding techniques in both a mean-square error (MSE) and a qualitative visual sense. ...
In this paper, we present two novel algorithms for denoising hyperspectral data. Each algorithm exploits correlation between bands by enforcing simultaneous sparsity on their wavelet representations. ...
Wavelets are a popular multiresolution analysis technique in the image compression, denoising and remote sensing communities. ...
doi:10.1109/igarss.2006.104
dblp:conf/igarss/ZelinskiG06
fatcat:zztsb3twnnernlvl4ac2dzbsre
Noise Reduction in Hyperspectral Imagery: Overview and Application
2018
Remote Sensing
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Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth's surface emitted by the Sun. ...
In this review, we focus on the hyperspectral cameras which provide the reflectance from a scanned scene. Remote Sens. 2018, 3, 482 3 of 28 3 5 23 62 94 ...
Introduction Remote sensing has been substantially influenced by hyperspectral imaging in the past decades [1] . ...
doi:10.3390/rs10030482
fatcat:zvlgnlgsjneg5fiu7d4btphzny
Anomaly targets detection of hyperspectral imagery based on wavelet transform and sparse representation
2018
MATEC Web of Conferences
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In order to overcome low efficiency of current anomaly target detection in hyperspectral image, an anomaly detection algorithm for hyperspectral images based on wavelet transform and sparse representation ...
Firstly, two-dimensional discrete wavelet transform is used to denoise the hyperspectral image, and the new hyperspectral image data are obtained. ...
In paper, wavelet transform is used to denoise hyperspectral images. ...
doi:10.1051/matecconf/201823202054
fatcat:4io7bkfffzea5ip5t2jeqtm7t4
ADMM based Hyperspectral Image Classification Improved by Denoising using Legendre Fenchel Transformation
2015
Indian Journal of Science and Technology
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This paper uses a fast and reliable denoising technique based on Legendre Fenchel Transformation (LFT) to effectively denoise each band of HSI prior to ADMM based classification (proposed method). ...
This paper discusses about a sparsity based algorithm used for Hyperspectral Image (HSI) classification where the test pixel vectors are sparsely represented as the linear combination of a few number of ...
The Gaussian noise present in the colour remote sensing image is effectively removed by using a denoising technique based on Legendre Fenchel Transformation 11 . ...
doi:10.17485/ijst/2015/v8i24/80030
fatcat:y72mzyfysvfuvdggche2aimi3m
Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain
2020
Remote Sensing
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The presence of noise in remote sensing satellite images may cause limitations in analysis and object recognition. ...
We also used hyperspectral remote sensing images from AVIRIS, HYDICE, and ROSIS sensors for our experimental analysis and validation process. ...
The results indicated that the proposed method acts promisingly in de-noising the hyperspectral remote sensing images as well. ...
doi:10.3390/rs13010101
fatcat:pwbuf4c6vbdnvlbsluaktqhoty
Recent advances in remote sensing image processing
2009
2009 16th IEEE International Conference on Image Processing (ICIP)
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This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing. ...
Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. ...
Fig. 1 . 1 Remote sensing image processing chain.
Table 1 . 1 A taxonomy for remote sensing methods and applications. ...
doi:10.1109/icip.2009.5414281
dblp:conf/icip/TuiaC09
fatcat:7daxlzqb55buhpcsiffwpd7m3q
Table of contents
2020
IEEE Geoscience and Remote Sensing Letters
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Feature Extraction of Hyperspectral Images Based on Deep Boltzmann Machine ..... J. Yang, Y. Guo, and X. ...
Yin 1082 Transfer Learning for Remote Sensing Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks ............ ...
doi:10.1109/lgrs.2020.2992096
fatcat:pc6t3mszzjhg5lkk5dxnhx4b2y
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