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Band selection using independent component analysis for hyperspectral image processing

Hongtao Du, Hairong Qi, Xiaoling Wang, R. Ramanath, W.E. Snyder
32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings.  
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


A.Kiranmai .
2016 International Journal of Research in Engineering and Technology  
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

Yingxuan Zhu, Pramod K. Varshney, Hao Chen
2007 2007 IEEE International Conference on Image Processing  
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

Shrutika Sawant, Manoharan Prabukumar
2020 Journal of Spectral Imaging  
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

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

Mojtaba Amini Omam, Farah Torkamani-Azar
2010 Optical Review  
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

B. Raviteja, M. Surendra Prasad Babu, K. Venkata Rao, J. Harikiran
2016 Indian Journal of Science and Technology  
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

Stefan A. Robila, Sylvia S. Shen, Paul E. Lewis
2003 Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX  
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

Stefan A. Robila, Sylvia S. Shen, Paul E. Lewis
2004 Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X  
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

Juntao Xiong, Rui Lin, Rongbin Bu, Zhen Liu, Zhengang Yang, Lianyi Yu
2018 Sensors  
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

Secil Suer, Hatice Koc, Sarp Erturk
2014 2014 22nd Signal Processing and Communications Applications Conference (SIU)  
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

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

Murinto, D P Ismi
2019 IOP Conference Series: Materials Science and Engineering  
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

Zhongzhi Han, Jianhua Wan, Limiao Deng, Kangwei Liu, Adrian G Dyer
2016 PLoS ONE  
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

Zahra Dabiri, Stefan Lang
2018 ISPRS International Journal of Geo-Information  
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
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