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Spectral-Spatial Constraint Hyperspectral Image Classification
2014
IEEE Transactions on Geoscience and Remote Sensing
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Target detection is an important issue in the HyperSpectral Image (HSI) processing field. ...
Then, to jointly filter a component tensor in each mode, multiway Wiener filter (MWF) is introduced. Moreover, to determine the best transform level and basis of 3-WPT a risk function is proposed. ...
In this paper, the main idea is to decompose a hyperspectral image into different coefficient tensors and jointly filter each of these tensors in three modes. ...
doi:10.1109/tgrs.2013.2255297
fatcat:2l5tncn7fjcp7hlhetq5244ety
Small Target Detection Improvement in Hyperspectral Image
[chapter]
2013
Lecture Notes in Computer Science
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Target detection is an important issue in the HyperSpectral Image (HSI) processing field. ...
This paper utilizes the recently proposed multidimensional wavelet packet transform with multiway Wiener filter (MWPT-MWF) to improve the target detection efficiency of HSI with small targets in the noise ...
It decomposes the HSI into different coefficient tensors (components) by wavelet packet transform [6] , and jointly filter each component by MWF. ...
doi:10.1007/978-3-319-02895-8_41
fatcat:ettngsmzoveqtn62gal3j3cu2m
Enhanced visualization of hyperspectral images
2010
2010 IEEE International Geoscience and Remote Sensing Symposium
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In [8] a tensor based method is proposed to jointly take advantage of spatial and spectral information for dimensionality reduction. ...
L R = r T L i L G = g T L i L B = b T L i (4) By applying the inverse wavelet transform, the R,G and B components of the composite RGB image are obtained. ...
doi:10.1109/igarss.2010.5652813
dblp:conf/igarss/MahmoodS10
fatcat:g2ga37mbyre57fxfonk2xxu2lq
Enhanced Visualization of Hyperspectral Images
2011
IEEE Geoscience and Remote Sensing Letters
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In [8] a tensor based method is proposed to jointly take advantage of spatial and spectral information for dimensionality reduction. ...
L R = r T L i L G = g T L i L B = b T L i (4) By applying the inverse wavelet transform, the R,G and B components of the composite RGB image are obtained. ...
doi:10.1109/lgrs.2011.2125775
fatcat:o3u25uehinbvnozzcu6fhpazgy
Survey of hyperspectral image denoising methods based on tensor decompositions
2013
EURASIP Journal on Advances in Signal Processing
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And the third one is the combination of multidimensional wavelet packet transform (MWPT) and MWF (MWPT-MWF), which models each coefficient set as a tensor and then filters each tensor by applying MWF. ...
A hyperspectral image (HSI) is always modeled as a three-dimensional tensor, with the first two dimensions indicating the spatial domain and the third dimension indicating the spectral domain. ...
After this step, each component is filtered by MWF automatically. ...
doi:10.1186/1687-6180-2013-186
fatcat:hda24x3x4rejna6shkuginsavm
HYPERSPECTRAL IMAGE MIXED NOISE REDUCTION BASED ON IMPROVED K-SVD ALGORITHM
2014
International Journal of Research in Engineering and Technology
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We propose an algorithm for mixed noise reduction in Hyperspectral Imagery (HSI). The hyperspectral data cube is considered as a three order tensor. ...
This method of denoising can efficiently remove a variety of mixed or single noise by applying sparse regularization of small image patches. It also maintains the image texture in a clear manner. ...
Some of the traditional denoising algorithms are channel by channel, singular value decomposition (SVD), Wiener and wavelet filters. ...
doi:10.15623/ijret.2014.0319151
fatcat:nlt3agkkeneznpt4qgfqwgbldy
Hyperspectral Classification of Two-Branch Joint Networks Based on Gaussian Pyramid Multiscale and Wavelet Transform
2022
IEEE Access
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INDEX TERMS Hyperspectral image, Gaussian pyramid, wavelet transforms, hyperspectral image classification. ...
Hence, the spectral data are processed by wavelet transform to reduce the influence of intra-class spectral variation on classification. ...
SPECTRAL FEATURE EXTRACTION For spectral features, we sample wavelet transform for each image element of the hyperspectral image, because of weather, atmosphere, light or satellite sensor imaging process ...
doi:10.1109/access.2022.3172501
fatcat:45uhpmc4qjgdvb4jsi3cgkaev4
Hyperspectral image noise reduction based on rank-1 tensor decomposition
2013
ISPRS journal of photogrammetry and remote sensing (Print)
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A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensors using an eigenvalue intensity sorting and reconstruction technique. ...
The hyperspectral data cube is considered as a three-order tensor that is able to jointly treat both the spatial and spectral modes. ...
This work was supported in part by the Natural Science Foundation of China (41101336), in part by the Program for New Century Excellent Talents in University of China (NCET-11-0396), and in part by the ...
doi:10.1016/j.isprsjprs.2013.06.001
fatcat:n4xioih5zjhzfhr2tmrfqqgezy
Improvement of Classification Based on Noise and Spectral Dimensionality Reduction for Hyperspectral Image
2018
Geoscience and Remote Sensing
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Hyperspectral image (HSI) classification requires spectral dimensionality reduction and spatial filtering. ...
Then we propose a method based on quadtree decomposition adapted to tensor data in order to take into account the local image characteristics in the multi-way Wiener filter (LMWF) which performs both noise ...
Equation (11) represents the n-mode filtering of data tensor R by n-mode filters H (n) , n = 1 to 3. ...
doi:10.23977/geors.2018.11012
fatcat:xp5ktmmhczdv5ia45vzn3yw2yu
A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES
2017
International Journal on Smart Sensing and Intelligent Systems
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The spectral and spatial information reflected from the original Hyperspectral Images with four various features. ...
A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. ...
The Hyperspectral images are processed by 3D-Gabor filters to obtain the wavelength properties, scale and directions in an effective way. ...
doi:10.21307/ijssis-2017-224
fatcat:k2x24hgfkjctxh3jwjssq5esle
A Survey of Multi Sensor Satellite Image Fusion Techniques
2020
International Journal of Sensors and Sensor Networks
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Image fusion can also be used for providing some protection against illegal copying by embedding water-marks. ...
The objective of image fusion is to produce a single image containing the best aspects of the fused images. ...
processing large hyperspectral images. ...
doi:10.11648/j.ijssn.20200801.11
fatcat:l7uzbwz55jg67keb6wxznvgh2u
Wavelet packets for time-frequency analysis of multispectral imagery
2013
GEM - International Journal on Geomathematics
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Finally, the wavelet packets coefficients undergo a dimension reduction process. We present examples of this theory applied to hyperspectral satellite imagery. ...
Each spectral band is individually decomposed by the wavelet packets transform, and then the entropy term is jointly guided by information from all bands, simultaneously. ...
Acknowledgment This work presented in this paper was supported in part by NSF (CBET 0854233), by NGA (HM 15820810009), by NIH/DFG (EH 405/1-1/575910), by WWTF (VRG 12-009), and by MURI-ARO (W911NF-09-0383 ...
doi:10.1007/s13137-013-0052-y
fatcat:4ylhjnhwbbd4hmza3uzdjmchmy
A Nonlocal Structure Tensor-Based Approach for Multicomponent Image Recovery Problems
2014
IEEE Transactions on Image Processing
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2014 pre-print arXiv:1403.5403v1 fatcat:sxa5oqvkezacnlpebpijiynlziOther Versions
In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the Structure Tensor (ST) resulting from the gradient of a multicomponent image. ...
This formulation can be efficiently implemented thanks to the flexibility offered by recent primal-dual proximal algorithms. Experiments are carried out for multispectral and hyperspectral images. ...
An alternative way is to process the components jointly, so as to better reveal details and features that are not visible in each of the components considered separately. ...
doi:10.1109/tip.2014.2364141
pmid:25347882
fatcat:mz3pjto6mrbspbpgkg43snf6ua
Hyperspectral denoising based on the principal component low-rank tensor decomposition
2022
Open Geosciences
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First, we use PCA to reduce the dimension of HSI signals by obtaining the first K principal components and get the principal composite components. ...
Due to the characteristics of hyperspectral images (HSIs), such as their high spectral resolution and multiple continuous narrow bands, HSI technology has become widely used in fields such as target recognition ...
for HSI denoising, such as wavelet transforms [4] , principal component analysis (PCA) [5] , and sparse 3D transform-domain collaborative filtering (BM3D) method [3] . ...
doi:10.1515/geo-2022-0379
fatcat:ghqvlj677fe5nahgjrhbyaylxu
Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising
2015
2015 IEEE International Conference on Computer Vision (ICCV)
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However, hyperspectral images often times suffer from degradation due to the limited light, which introduces noise into the imaging process. ...
Hyperspectral imaging is beneficial in a diverse range of applications from diagnostic medicine, to agriculture, to surveillance to name a few. ...
Introduction Hyperspectral imaging is the process of capturing images of a scene over multiple bands of the electromagnetic spectrum. ...
doi:10.1109/iccv.2015.47
dblp:conf/iccv/FuLSS15
fatcat:kimhc5f5dfdrzapmduvvnv3mui
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