Band Selection for Dimension Reduction in Hyper Spectral Image Using Integrated InformationGain and Principal Components Analysis Technique

Koonsanit, Kitti; Jaruskulchai, Chuleerat; Eiumnoh, Apisit

doi:10.7763/ijmlc.2012.v2.124

Cited by 66 publications

(26 citation statements)

References 3 publications

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“…In many works the BandClust method gives higher results in comparison with other unsupervised reduction methods tested on hyperspectral data (AVIRIS, ROSIS, CASI) [27].…”

Section: A Unsupervised Bands Selection Phasementioning

confidence: 99%

“…Hybrid methods are proposed in the literature for combining bands selection methods with projection methods. In fact, in [27], a new approach was proposed, where the PCA method was combined with a bands selection method based on MI. In [28], a hybrid approach was proposed, indeed, the MDS projection method was combined with unsupervised bands selection method called BandClust.…”

Section: Unsupervised Bands Selection Phasementioning

confidence: 99%

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Interpretation of hyperspectral imagery based on hybrid dimensionality reduction methods

Sellami

Ettabaa

Farah

et al. 2014

International Image Processing, Applications and Systems Conference

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The interpretation of hyperspectral imagery is an essential task for classification, changes detection and monitoring of natural phenomena. One challenge of the processing hyperspectral images, with better spectral and temporal resolution is the huge amount of data volume and the interpretation in high dimensionality data. Various techniques have been developed in the literature for dimensionality reduction, generally divided into two main categories: projection techniques and bands selection techniques. In this work, we present a new approach for interpretation in hyperspectral imagery based on hybrid dimensionality reduction methods. The presented approach combines a projection method with a bands selection method. Indeed, the objective of the research is to obtain an efficient hyperspectral image interpretation. The performances of the proposed approach were evaluated using AVIRIS hyperspectral image.

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“…In many works the BandClust method gives higher results in comparison with other unsupervised reduction methods tested on hyperspectral data (AVIRIS, ROSIS, CASI) [27].…”

Section: A Unsupervised Bands Selection Phasementioning

confidence: 99%

Section: Unsupervised Bands Selection Phasementioning

confidence: 99%

Interpretation of hyperspectral imagery based on hybrid dimensionality reduction methods

Sellami

Ettabaa

Farah

et al. 2014

International Image Processing, Applications and Systems Conference

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“…Principal component analysis (PCA) [8], [9] is widely used linear dimensionality reduction technique [10]. Band selection is an essential tool for identification of optimal spectral for different satellite applications.…”

Section: Pca Based Dimension Reductionmentioning

confidence: 99%

Feature extraction of hyperspectral image using principal component analysis and folded-principal component analysis

Deepa¹,

Thilagavathi²

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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Hyperspectral imaging is one of the advanced remote sensing techniques. High dimensional nature of hyperspectral image makes its analysis complex. Various methods have been developed to reduce the dimension of hyperspectral image. Most commonly used dimension reduction technique is Principal Component Analysis (PCA), which is a feature extraction method. The main shortcoming of PCA method is that it does not consider the local structures. Folded-PCA (F-PCA) takes into account both global and local structures, while preserving all useful properties of PCA. This paper presents comparative study of PCA and Folded-PCA approach for feature extraction of hyperspectral image.

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“…It reduces the high dimensional vectors to a set of lower dimensional vectors (Koonsanit et al, 2012). Number of bands of original dataset is reduced into a number of new bands that is called the principle components to increase the covariance and decrease redundancy in order to achieve lower dimensionality.…”

Section: Figure 5 : Region Of Interest (Roi) (Ii) Principal Componentmentioning

confidence: 99%

Oil Spill Hyperspectral Data Analysis: Using Minimum Distance and Binary Encoding Algorithms

El_Rahman¹

2017

IJCNT

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Abstract:Oil spill calamities have increased, threating maritime ecosystems. This reinforces the need for accurate mapping of oilspill calamities. The use of hyperspectral classifiers to extract areas of oil spill in a test site was achieved in this work. The paper describes the effects of utilizing a set of hyperspectral image analysis algorithms such as Minimum Distance (MD) and Binary Encoding (BE) algorithms to classify hyperspectral images of oil-spill areas in the Gulf of Mexico using Environment for Visualizing Images software. Hyperspectral image subseting, region of interest and principal component analysis were performed in the preprocessing stage, which is used to reduce the vast amount of data and eliminate redundant data. The paper provides empirical insights on the classification accuracy of hyperspectral images. A confusion matrix is used to determine the accuracy of a classification by comparing a classification result with ground truth information. The overall accuracies were 94.6399% and 88.4422% for the MD and BE algorithms, respectively. Therefore, the two algorithms are accurate for classifying hyperspectral images of the Gulf of Mexico. However, the MD algorithm is more accurate than the BE algorithm.

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Band Selection for Dimension Reduction in Hyper Spectral Image Using Integrated InformationGain and Principal Components Analysis Technique

Cited by 66 publications

References 3 publications

Interpretation of hyperspectral imagery based on hybrid dimensionality reduction methods

Interpretation of hyperspectral imagery based on hybrid dimensionality reduction methods

Feature extraction of hyperspectral image using principal component analysis and folded-principal component analysis

Oil Spill Hyperspectral Data Analysis: Using Minimum Distance and Binary Encoding Algorithms

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