A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2004; you can also visit the original URL.
The file type is application/pdf.
Filters
Band selection using independent component analysis for hyperspectral image processing
32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings.
Preserved Fulltext
In this paper, we present a band selection method based on Independent Component Analysis (ICA). ...
The experimental results show that ICA-based band selection is more effective in dimensionality reduction for HSI analysis. ...
Viewpoint invariant face recognition using independent component analysis and attractor networks, chapter Neural Information Processing independent bands in reflectance spectrum. ...
doi:10.1109/aipr.2003.1284255
dblp:conf/aipr/DuQWRS03
fatcat:cmuqq7combgtfjcd7x5gvst24q
DIMENSIONALITY REDUCTIONTECHNIQUES FORANALYSIS OF HYPERSPECTRAL DATA
2016
International Journal of Research in Engineering and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
So when we use all the bands for processing it increases the computational complexity in terms of storage and processing time. ...
Data redundancy is one of the problems when processing hyperspectral data. And some bands in hyperspectral images are noisy and some bands are set to zero. ...
Independent Component Analysis Independent Component Analysis (ICA) is technique which is based on the assumption that the each band in the input data that we consider for processing is an independent ...
doi:10.15623/ijret.2016.0519002
fatcat:oivcjwvuprc6taue7lygatdkoq
Dimensionality Reduction of Hyperspectral Images for Color Display using Segmented Independent Component Analysis
2007
2007 IEEE International Conference on Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper, several independent component analysis (ICA) based approaches are proposed to reduce the dimensionality of hyperspectral images for visualization. ...
We also develop a simple but effective method, based on correlation coefficient and mutual information (CCMI), to select the suitable independent components for RGB color representation. ...
independent components and hyperspectral bands. ...
doi:10.1109/icip.2007.4379963
dblp:conf/icip/ZhuVC07
fatcat:wswiqktuzbeqxmltrpeoahzw64
A survey of band selection techniques for hyperspectral image classification
2020
Journal of Spectral Imaging
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Our purpose is to highlight the progress attained in band selection techniques for hyperspectral image classification and to identify possible avenues for future work, in order to achieve better performance ...
Hyperspectral images usually contain hundreds of contiguous spectral bands, which can precisely discriminate the various spectrally similar classes. ...
grant for carrying out this research work. ...
doi:10.1255/jsi.2020.a5
fatcat:cvibjoofbbd6jpu4ij626wigdy
A Research: Hyperspectral Image Processing Techniques
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Hyperspectral image processing is a complicated process which rely on mixed agents. ...
An basic problems in hyperspectral image processing are dimension reduction, target detection, target identification, and target classification. ...
Independent Component Analysis (ICA) Independent-component analysis (ICA) is a popular technique for unsupervised classification [6] . ...
doi:10.35940/ijitee.i1120.0789s219
fatcat:unshblappfbvrc33eocjxkq5vy
Band selection of hyperspectral-image based weighted indipendent component analysis
2010
Optical Review
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Huge amounts of data in hyperspectral images have been caused to represent approaches for the band selection of these images. ...
In this paper, a new approach based on independent component analysis (ICA) is proposed. The idea of projection pursuit is used to order the bands on the basis of a non-gaussianity distribution. ...
Conclusions In this paper, we propose a new method based on independent component analysis (ICA) for band selection in hyperspectral images. ...
doi:10.1007/s10043-010-0067-7
fatcat:u5svcammnvasznvjts42v7ebpu
Framework for Hyperspectral Image Segmentation using Unsupervised Algorithm
2016
Indian Journal of Science and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
This paper presents a framework for hyperspectral image segmentation using a clustering algorithm. The framework consists of four stages in segmenting a hyperspectral data set. ...
The main goal of image fusion is to combine all the information from the selected image bands to form a single image. This single image is segmented using Fuzzy C-means (FCM) algorithm. ...
The image band is reconstructed by combining the filtered components and non-filtered components. The same procedure is used for filtering all the image bands. ...
doi:10.17485/ijst/2015/v8i1/107931
fatcat:dixucfgcyncg7a2i7gjgtnndre
Investigation of spectral screening techniques for independent-component-analysis-based hyperspectral image processing
2003
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
This paper investigates the effect of spectral screening on processing hyperspectral data through Independent Component Analysis (ICA). ...
ICA is a multivariate data analysis method producing components that are statistically independent. ...
This model is similar to the one used for applying Principal Component Analysis on multispectral / hyperspectral images [9] . ...
doi:10.1117/12.487091
fatcat:ofsu2w45tbgvdcgb2gzi6iccqq
Distributed source separation algorithms for hyperspectral image processing
2004
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf.
I use Independent Component Analysis (ICA), a particular case of BSS, where, given a linear mixture of statistical independent sources, the goal is to recover these components by producing the unmixing ...
This paper describes a new algorithm for feature extraction on hyperspectral images based on blind source separation (BSS) and distributed processing. ...
Among the choices for preprocessing techniques, one of the widely used is Principal Component Analysis (PCA). ...
doi:10.1117/12.541892
fatcat:xn7apjzmvbgmbcodqefhcuitcy
A Micro-Damage Detection Method of Litchi Fruit Using Hyperspectral Imaging Technology
2018
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Then the hyperspectral data samples of fresh and micro-damaged litchi fruits were selected, and a partial least squares discriminant analysis (PLS-DA) was used to establish a prediction model for the realization ...
Principal component analysis (PCA) was used to determine the sensitive wavelength for recognition of litchi quality characteristics, with the results of wavelengths corresponding to the local extremum ...
., R.L. and R.B. contributed equally in the implementation of the experiments, data acquisition, experimental work, data analysis, and manuscript writing. ...
doi:10.3390/s18030700
pmid:29495421
pmcid:PMC5876671
fatcat:pha4szhherhijn7c2ihllbdm3i
Hyperspectral image visualization based on high dynamic range imaging
2014
2014 22nd Signal Processing and Communications Applications Conference (SIU)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
ABSTRACT Hyperspectral imaging captures a high number of spectrally narrow bands and provides advantages for image analysis applications such as identification and classification in particular. ...
However, for the visual inspection of hyperspectral images, the data is conventionally converted to a standard color image format. ...
Ramanath, "Band
selection using independent component analysis for
hyperspectral image processing," in Proc. 32nd Appl.
Imagery Pattern Recog. Workshop, Washington, DC, pp.
93-99, Oct. 2003. ...
doi:10.1109/siu.2014.6830442
dblp:conf/siu/SuerKE14
fatcat:gwiosktjwje6zno4xoyw36fd7u
Dimensionality Reduction Techniques For Hyperspectral Image using Deep Learning
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
extraction ICA(Independent Component Analysis) are adopted. ...
This Research proposal addresses the issues of dimension reduction algorithms in Deep Learning(DL) for Hyperspectral Imaging (HSI) classification, to reduce the size of training dataset and for feature ...
using ICA the dimensionality reduction procedure preprocessing of for feature extraction later the Independent Component Analysis (ICA) is used to classify the hyperspectral image sets into labels depending ...
doi:10.35940/ijitee.b1033.1292s319
fatcat:hillxk55sngt7o42xbtxpm4ea4
Hyperspectral image segmentation using discriminant independent component analysis and swarm optimization approaches
2019
IOP Conference Series: Materials Science and Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In this paper, a new approach to extraction features and independence of hyperspectral image proposes using Discriminant independent component analysis (DICA) and multilevel thresholding techniques based ...
Image segmentation is initial process of image analysis and recognition. ...
The author said thank you to DP2M-RISTEKDIKTI for the funds provided so that this research can be realized. ...
doi:10.1088/1757-899x/674/1/012054
fatcat:2k7m6yez3rg2hg7bdazg7lw2oi
Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA
2016
PLoS ONE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. ...
For comparison, algorithms such as plus L reduce R (plusLrR), Fisher, multidimensional scaling (MDS), independent component analysis (ICA), and principle component analysis (PCA) are also used to select ...
Independent component analysis (ICA) has been used to identify the single component spectra in glucose [16] . ...
doi:10.1371/journal.pone.0146547
pmid:26820311
pmcid:PMC4731151
fatcat:cufkjad22bhyhdoplbfqhxkgny
Comparison of Independent Component Analysis, Principal Component Analysis, and Minimum Noise Fraction Transformation for Tree Species Classification Using APEX Hyperspectral Imagery
2018
ISPRS International Journal of Geo-Information
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. ...
analysis (ICA), principal component analysis (PCA), and minimum noise fraction transformation (MNF), and stacking the selected dimensionality-reduced (DR) components to create new data cubes; (3) super-pixel ...
reduction techniques, namely, principal component analysis (PCA), minimum noise fraction (MNF), and independent component analysis (ICA); (4) segmentation of the results using super-pixel segmentation ...
doi:10.3390/ijgi7120488
fatcat:o6uyp7f5b5go3kkjhhwnr7awpm
« Previous
Showing results 1 — 15 out of 9,045 results