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A Research: Hyperspectral Image Processing Techniques
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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In this document, we reviewed the latest activities of target classification, most frequently used techniques for dimension reduction, target detection. ...
An basic problems in hyperspectral image processing are dimension reduction, target detection, target identification, and target classification. ...
ICA is skilled in classification, feature extraction and target detection in hyperspectral images [18] . ...
doi:10.35940/ijitee.i1120.0789s219
fatcat:unshblappfbvrc33eocjxkq5vy
Dimensionality Reduction Techniques For Hyperspectral Image using Deep Learning
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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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 ...
extraction ICA(Independent Component Analysis) are adopted. ...
CONCLUSION The Dimensionality Reduction of Hyperspectral Imaging using Deep Learning is an ICA-stationed feature extraction method that extracts independent components later detects targets for various ...
doi:10.35940/ijitee.b1033.1292s319
fatcat:hillxk55sngt7o42xbtxpm4ea4
Detection and Identification of Camouflaged Targets using Hyperspectral and LiDAR data
2018
Defence Science Journal
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This paper presents a framework for detection of camouflaged target that allows military analysts to coordinate and utilise the expert knowledge for resolving camouflaged targets using remotely sensed ...
This facilitates extraction of salient features of the potential camouflaged target. Lastly, the decisions obtained have been fused to infer the identity of the desired targets. ...
Figure 5 . 5 Image-II results (a) ICA component and (b) detection for CAmo1. ...
doi:10.14429/dsj.68.12731
fatcat:y5u7shef4be6fpumdck4k3obyu
Target detection in hyperspectral images based on independent component analysis
2002
Automatic Target Recognition XII
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The paper presents an algorithm based on Independent Component Analysis (ICA) for the detection of small targets present in hyperspectral images. ...
It then separates the features present in the image using an ICA based algorithm. The method involves a gradient descent minimization of the mutual information between frames. ...
In this paper, we explore the extent to which ICA can be applied to feature extraction and, in particular, to target detection in hyperspectral images. ...
doi:10.1117/12.477024
fatcat:5muxls5pu5fhnij7f3cusgr26m
Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration
2016
Journal of Food Engineering
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In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. ...
Finally, all the extracted ICs were used to construct a single syn-thetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours ...
Acknowledgements The authors are very grateful for the peanut samples sets used in the work provided by European Commission Joint Research Centre, Institute for Reference Materials and Measurements. ...
doi:10.1016/j.jfoodeng.2015.07.008
fatcat:xqc5wfrphbhadgirqoiuiqtno4
Subpixel target detection in hyperspectral data using higher order statistics source separation algorithms
2005
Computational Imaging III
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The targets are filtered using histogram-based analysis. The end result is a map of the pixels associated with small targets. ...
Hyperspectral data is modeled as an unknown mixture of original features (such as the materials present in the scene). ...
We include the steps presented above in a complete HOS / ICA based target detection algorithm. ...
doi:10.1117/12.586971
dblp:conf/cimaging/Robila05
fatcat:eah26audhba4npimrycnajb3mi
NEW APPROACHES FOR FEATURE EXTRACTION IN HYPERSPECTRAL IMAGERY
2006
2006 IEEE Long Island Systems, Applications and Technology Conference
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In this paper we introduce a novel feature extraction method based on Nonnegative Matrix Factorization (NMF) for hyperspectral image processing. ...
We present our results on using NMF for feature extraction by performing experiments with hyperspectral digital imagery collection experiment (HYDICE) data as well as in-house imagery collected with a ...
For the first experiment we used a hyperspectral image from the Hyperspectral Digital Imagery Collection Experiment (HYDICE). ...
doi:10.1109/lisat.2006.4302652
fatcat:2wdiirrzvvdsljsxtbto2ih7aq
Hyperspectral anomaly detection based on anomalous component extraction framework
2019
Infrared physics & technology
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In this paper, we propose a novel anomalous component extraction framework for hyperspectral anomaly detection based on Independent Component Analysis (ICA) and Orthogonal Subspace Projection (OSP). ...
Anomaly detection has become an important topic in Hyperspectral Imagery (HSI) analysis in the last two decades with the advantage of detecting the targets surrounding in diverse backgrounds without prior ...
ACKNOWLEDGEMENTS We would like to express our sincere appreciation to the anonymous reviewers for their insightful and valuable comments, which have greatly helped us in improving the quality of the paper ...
doi:10.1016/j.infrared.2018.12.008
fatcat:ywbfgcyowngfbaoesfaqfiavcy
Distributed source separation algorithms for hyperspectral image processing
2004
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
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ICA based methods have been employed for target detection and classification of hyperspectral images. However, these methods involve an iterative optimization process. ...
This paper describes a new algorithm for feature extraction on hyperspectral images based on blind source separation (BSS) and distributed processing. ...
CONCLUSIONS In this paper I have provided a novel distributed implementation of a source separation algorithm that can be used for feature extraction for hyperspectral imagery. ...
doi:10.1117/12.541892
fatcat:xn7apjzmvbgmbcodqefhcuitcy
The Novel Gravitational Mass Weighted PCA Technique for Feature Extraction in Hyperspectral Data Classification
2019
International Journal of Engineering and Advanced Technology
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The above issues are overcome using feature extraction and feature selection methods which play a major role in the reduction of dimensionality. ...
Also, this paper presents the deep insight about the feature extraction techniques in hyperspectral data of both supervised and unsupervised learning methods and experimental analysis in AVIRIS Indian ...
ICA, PCA, and MNF are often used in HS data as unsupervised feature extraction techniques [1] . ...
doi:10.35940/ijeat.e1056.0785s319
fatcat:w4fsxms6dbcmtkn4tbkfogzari
Unsupervised clustering and spectral unmixing for feature extraction prior to supervised classification of hyperspectral images
2011
Satellite Data Compression, Communications, and Processing VII
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In this paper, we develop a new strategy for feature extraction prior to supervised classification of hyperspectral images. ...
In previous work, we have demonstrated that spectral unmixing can be used as an effective approach for feature extraction prior to supervised classification of hyperspectral data using support vector machines ...
can be used as feature extraction for improving supervised classification of hyperspectral data via machine learning techniques is a novel contribution. ...
doi:10.1117/12.892469
fatcat:ujhvwhap5fetpfhq76mf4xmu7m
Special issue on advances in real-time image processing for remote sensing
2018
Journal of Real-Time Image Processing
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Carlsohn, of Journal of Real-Time Image Processing as well as the administrative staff for their support throughout the preparation and publication of this special issue. ...
We are also very grateful to the reviewers for their constructive suggestions to improve the quality of the papers. ...
[11] presents a real-time unsupervised background extraction-based target detection method which uses the endmember extraction to extract material signatures from the images. ...
doi:10.1007/s11554-018-0831-7
fatcat:6y2rq6jagjdzpjit65reyeev6m
Unsupervised Nonlinear Feature Extraction Method And Its Effects On Target Detection In High-Dimensional Data
2015
International Journal of Electrical Electronics and Data Communication
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In this paper, KPCA is applied as a feature extraction procedure to dimension reduction for target detection as a preprocessing on hyperspectral images. ...
Index Terms-Feature extraction, hyperspectral imagery, kernel principal component analysis, support vector data description, target detection. ...
INTRODUCTION In hyperspectral imaging, target detection is based on the material of the light absorption and reflection characteristics. ...
doi:10.18479/ijeedc/2015/v3i8/48353
fatcat:bhpbdieic5e7bkcjmivyw6mkyu
Hyperspectral endmember extraction using Pearson's correlation coefficient
2021
International Journal of Computational Science and Engineering (IJCSE)
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A critical step in this chain is endmember extraction which finds endmembers from the image for the estimation of abundances. ...
for endmember extraction (PCGE). ...
The authors of this paper are also thankful to the management of the Nirma University for providing the necessary infrastructure and support. ...
doi:10.1504/ijcse.2021.113656
fatcat:e76rea6fmfbkjiwqnhknhqzbxu
Low-bit rate exploitation-based lossy hyperspectral image compression
2010
Journal of Applied Remote Sensing
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In order to demonstrate advantages of the proposed spectral/spatial compression approach applications of subpixel target detection and mixed pixel analysis are used for experiments for performance evaluation ...
pure pixel-based image compression are often overlooked in such a 2D-to-3D compression. ...
Acknowledgments The authors would like to acknowledge the use of the QccPack developed by Dr. J.E. Fowler with the Mississippi State University for the experiments conducted in this work. ...
doi:10.1117/1.3530429
fatcat:2f72oxaiwbd2tju45umqfy5zyi
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