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Nonlinear Spectral Image Fusion
[chapter]
2017
Lecture Notes in Computer Science
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In this paper we demonstrate that the framework of nonlinear spectral decompositions based on total variation (TV) regularization is very well suited for image fusion as well as more general image manipulation ...
We conclude that the proposed spectral TV image decomposition framework is a valuable tool for semi-and fullyautomatic image editing and fusion. ...
MBe further acknowledges support from the Leverhulme Trust early career fellowship "Learning from mistakes: a supervised feedback-loop for imaging applications" and the Newton Trust. ...
doi:10.1007/978-3-319-58771-4_4
fatcat:xizsbwr52bggzi6dps36oti2zi
Nonlinear Spectral Image Fusion
[article]
2017
arXiv
pre-print
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In this paper we demonstrate that the framework of nonlinear spectral decompositions based on total variation (TV) regularization is very well suited for image fusion as well as more general image manipulation ...
We conclude that the proposed spectral TV image decomposition framework is a valuable tool for semi- and fully-automatic image editing and fusion. ...
MBe further acknowledges support from the Leverhulme Trust early career fellowship "Learning from mistakes: a supervised feedback-loop for imaging applications" and the Newton Trust. ...
arXiv:1703.08001v1
fatcat:k4z2lwworvdbfp3fdqctxjppza
Multiplicative Iterative Nonlinear Constrained Coupled Non-negative Matrix Factorization (MINC-CNMF) for Hyperspectral and Multispectral Image Fusion
2021
International Journal of Advanced Computer Science and Applications
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Keywords-Hyperspectral data; multispectral data; minimum volume; nonlinear mixing model; spectral variability; spectral image fusion 523 | P a g e www.ijacsa.thesai.org ...
the spatial quality of the image by considering the nonlinearity factor associated with the unmixing process of in the image. ...
distortion in the image, 3) propose an efficient image fusion algorithm that consider the nonlinearity effects in the image. ...
doi:10.14569/ijacsa.2021.0120660
fatcat:bmpvh3vwubdq5hwebyuqmof7pq
Fusion of hyperspectral and panchromatic images: A hybrid use of indusion and nonlinear PCA
2012
2012 19th IEEE International Conference on Image Processing
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One of the main challenges in hyperspectral image fusion is the improvement of the spatial resolution, i.e. spatial details while preserving the original spectral information. ...
This can be achieved by making use of a high spatial resolution PAN image in the context of pansharpening or image fusion. Several fusion approaches have been proposed in the literature. ...
A fusion of two images having different objects represented in them may lead to non acceptable spectral and spatial distortions. ...
doi:10.1109/icip.2012.6467314
dblp:conf/icip/LicciardiKC12
fatcat:qnvpov7ubzhs7kg5htaqzgzoiq
Hyperspectral and Multispectral Image Fusion using Cluster-based Multi-branch BP Neural Networks
2019
Remote Sensing
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Facing the above problem, the fusion of HMS with LHS image is formulated as a nonlinear spectral mapping from an HMS to HHS image with the help of an LHS image, and a novel cluster-based fusion method ...
Then, the spectrum-pairs from the clustered LMS image and the corresponding LHS image are used to train multi-branch BP neural networks (BPNNs), to establish the nonlinear spectral mapping for each cluster ...
In the fusion stage shown in Figure 3 , after an associative spectral clustering on the HMS image, each spectrum in the target HHS image is reconstructed by using the trained nonlinear spectral mapping ...
doi:10.3390/rs11101173
fatcat:eahdnzbejrc57gnlbuukktzoxa
Cross-sensor image fusion and spectral anomaly detection
2002
Signal Processing, Sensor Fusion, and Target Recognition XI
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A nonlinear mean square estimation algorithm for cross-sensor image fusion and spectral anomaly detection is described. ...
The algorithm can be used to enhance a low resolution image with a higher resolution coregistered multispectral image, and to detect anomalies between spectral bands (features in one spectral band that ...
The nonlinear estimator effectively segments an image into K different background types (spectral vectors). ...
doi:10.1117/12.477626
fatcat:o3ilyjewavdfriec2nzwmhs5sa
Deep Learning-Based Detail Map Estimation for MultiSpectral Image Fusion in Remote Sensing
[article]
2021
arXiv
pre-print
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This estimation is conducted as part of the component substitution approach for fusion of PANchromatic and MultiSpectral images in remote sensing. ...
After computing the band dependent intensity components, a deep neural network is trained to learn the nonlinear relationship between a PAN image and its nonlinear intensity components. ...
This network learns the nonlinear relationship between the intensity components of the spectral bands and its corresponding histogram matched PAN image. ...
arXiv:2102.03830v1
fatcat:3kfrr22v3zeybic2yfdr3xn2m4
Fusion of Hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction
2011
2011 IEEE International Geoscience and Remote Sensing Symposium
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enhance the spatial resolution of a hyperspectral image. ...
This paper presents a novel method for the enhancement of spatial quality of Hyperspectral (HS) images while making use of a high resolution panchromatic (PAN) image. ...
IMAGE FUSION The fusion of HS and PAN images is a useful technique for enhancing the spatial quality of low-resolution images. Generally, the fusion process can be subdivided into two steps. ...
doi:10.1109/igarss.2011.6049466
dblp:conf/igarss/LicciardiKCMCJ11
fatcat:njimy5jmwredpnok2jx6wyf2ci
Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction
2012
EURASIP Journal on Advances in Signal Processing
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enhance the spatial resolution of a hyperspectral image. ...
This paper presents a novel method for the enhancement of spatial quality of Hyperspectral (HS) images while making use of a high resolution panchromatic (PAN) image. ...
IMAGE FUSION The fusion of HS and PAN images is a useful technique for enhancing the spatial quality of low-resolution images. Generally, the fusion process can be subdivided into two steps. ...
doi:10.1186/1687-6180-2012-207
fatcat:gf4heneytfdphjuet6vgr7ajoi
Practical image fusion method based on spectral mixture analysis
2010
Science China Information Sciences
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Keywords image fusion, spectral mixture analysis, constrained nonlinear optimization Citation Yang W, Chen J, Matsushita B, et al. Practical image fusion method based on spectral mixture analysis. ...
Conventional image fusion algorithm, such as IHS, SVR, PCS, etc., may show some defects in inheriting the higher-spectral information embedded in the original lower-spatial resolution MS image. ...
In this study, an improved image fusion method based on spectral mixture analysis (IFSMA) is developed, in which the objective function of the nonlinear optimization expressions was modified to overcome ...
doi:10.1007/s11432-010-3118-6
fatcat:o2renvgmbvhafow37khski6znm
Multi-resolution analysis techniques and nonlinear PCA for hybrid pansharpening applications
2015
Multidimensional systems and signal processing
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In general, the CS methods result in fused images having high spatial quality but the fused images suffer from spectral distortions. ...
On the other hand, images obtained using MRA techniques are not as sharp as CS methods but they are spectrally consistent. ...
While SAM is useful for measuring the spectral quality of the fusion process, ERGAS can measure both spectral and spatial quality. ...
doi:10.1007/s11045-015-0359-y
fatcat:d6fbfhqxo5ejtgprfg3z4f5hw4
APPLICATION OF FUSION WITH SAR AND OPTICAL IMAGES IN LAND USE CLASSIFICATION BASED ON SVM
2012
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Two different fusion methods were used, one is joining SAR image into multi-spectral images as one band, and the other is direct fusing the two kind images. ...
The multi-spectral optical images express spectral features of ground objects, while SAR images express backscatter information. ...
The emergence of the hybrid image fusion such as Wavelet Transformation solved the problem of the spectral distortion in the image fusion. ...
doi:10.5194/isprsarchives-xxxix-b1-11-2012
fatcat:m34apl66pjc6la5dj75pc5kizu
Rank-Level Fusion of Multispectral Palmprints
2012
International Journal of Computer Applications
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The results of using rank level fusion on the publicly available multispectral palmprint database show the significant improvement in the recognition rate as compared to the individual spectral bands. ...
Recognition rate of 99.4% from sigmoid features and that of 99.2% from fuzzy features based on Rank 1 is the outcome of the hyperbolic tangent nonlinearity. ...
A comparison with the previous results is not possible as there is no work reported on rank level fusion of spectral images of palmprints. ...
doi:10.5120/4582-6761
fatcat:xoiy3kbklvhhbo3vgdlam5kfmq
Image fusion and spectral unmixing of hyperspectral images for spatial improvement of classification maps
2012
2012 IEEE International Geoscience and Remote Sensing Symposium
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In this paper we propose a new approach for the improvement of the spatial resolution of hyperspectral image classification maps combining both spectral unmixing and pansharpening approaches. ...
pansharpened image to find the location of each endmember within the enhanced pixel according to the endmembers abundances. ...
Pansharpening, or image fusion, is the process of improving the spatial quality of a low spatial resolution image by fusing it with a high resolution PAN image. ...
doi:10.1109/igarss.2012.6351978
dblp:conf/igarss/LicciardiVKC12
fatcat:y3bhygvwvjatngwyaipaj4ps34
A Hyperspectral Image Classification Approach Based on Feature Fusion and Multi-Layered Gradient Boosting Decision Trees
2020
Entropy
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This paper proposes a feature fusion and multi-layered gradient boosting decision tree model (FF-DT) for hyperspectral image classification. ...
First, we fuse extended morphology profiles (EMPs), linear multi-scale spatial characteristics, and nonlinear multi-scale spatial characteristics as final features to extract both special and spectral ...
The fusion of the three features fully embodies the spatial and spectral features of the hyperspectral images and the spectral information of the datasets. ...
doi:10.3390/e23010020
pmid:33375698
fatcat:kxixog6rqngrjkggvehbdrvliy
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