Image Fusion in Hyperspectral Image Classification using Genetic Algorithm

Saichandana, B.; Srinivas, Kudipudi; KiranKumar, R.

doi:10.11591/ijeecs.v2.i3.pp703-711

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…The first IMF is a high frequency component and the subsequent IMFs contain from next high frequency to the low frequency components. The shifting process [6] used to obtain IMFs on a 2-D signal (image) is summarized as follows: a) Let I(x,y) be a Remote Sensing Image used for EMD decomposition. Find all local maxima and local minima points in I(x,y).…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

Kumar

Saichandana

Srinivas

2016

IJEECS

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Section: Empirical Mode Decompositionmentioning

confidence: 99%

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

Kumar

Saichandana

Srinivas

2016

IJEECS

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“…Object detection and tracking [1,2], segmentation [3][4][5], edge detection [6], classification [7][8][9] and face recognition [10,11] are the main steps of a computer vision system for image analysis. The purpose of segmentation is to simplify and or change the representation of an image into something that is more meaningful and easier to analyze.…”

Section: Introductionmentioning

confidence: 99%

An iterative algorithm for color space optimization on image segmentation

Moussa¹

2021

TELKOMNIKA

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This paper proposes, a novel hybrid color component (HCC) issued from amounts number of color space with iterative manner, in fact traditional images obtained by RGB sensor weren't the effective way in image processing applications, for this purpose we have propose a supervised algorithm to substitute RGB level by hybrid and suitable color space at the aim to make well representation of the handled amounts of data, this step is extremely important because the obtained results it will be injected in many future studies like tracking, classification, steganography and cryptography. The second part of this paper consists to segment image coded in hybrid color space already selected, the used algorithm is inspired from kernel function where statistical distribution was used to model background and Bayes rule to make decision of the membership of each pixel, in this research topics we have extended this algorithm in the aim to improve compactness of these distribution. Cauchy background modeling and subtraction is used, and shows the high accuracy of automatic player detection.

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“…Proposed Methodlogy: This paper uses principal component analysis (PCA) as a feature extraction technique to extract the most informative band called principal component (PC). Bi-dimensional empirical mode decomposition (BEMD) [11] signal processing method is used to fragment the principle component into non-destructive hierarchical components called bi-dimensional intrinsic mode functions (BIMFs) and Residue image. These BIMFs are non-stationary and non-linear functions resulted from sifting process.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral image classification using support vector machines

Harikiran

2020

IJ-AI

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In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.

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Image Fusion in Hyperspectral Image Classification using Genetic Algorithm

Abstract: Abstract

Cited by 8 publications

References 10 publications

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

An iterative algorithm for color space optimization on image segmentation

Hyperspectral image classification using support vector machines

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