Clustering Algorithm Combined with Hill Climbing for Classification of Remote Sensing Image

Chandana, Bolem Sai; Srinivas, Kudipudi; Kumar, R. Kiran

doi:10.11591/ijece.v4i6.6608

Cited by 20 publications

(17 citation statements)

References 7 publications

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“…The flow process of Fuzzy C-Means Algorithm [7] is shown in figure 6. The flow process of K-Medoids Algorithm [9] is shown in figure 7.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

Dimensionality Reduction using Bi-Dimensional Empirical Mode Decomposition Method for Hyperspectral Image Segmentation

Teja*,

Rao

2019

IJRTE

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This paper presents a dimensionality reduction of hyperspectral dataset using bi-dimensional empirical mode decomposition (BEMD). This reduction method is used in a process for segmentation of hyperspectral data. Hyperspectral data contains multiple narrow bands conveying both spectral and spatial information of a scene. Analysis of this kind of data is done in three sequential stages, dimensionality reduction, fusion and segmentation. The method presented in this paper mainly focus on the dimensionality reduction step using BEMD, fusion is carried out using hierarchical fusion method and the segmentation is carried out using Clustering algorithms. This dimensionality reduction removes less informative bands in the data set, decreasing the storage and processing load in further steps in analysis of data. The qualitative and quantitative analysisshows that best informative bands are selected using proposed method which gets high quality segmented image using FCM.

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“…The flow process of Fuzzy C-Means Algorithm [7] is shown in figure 6. The flow process of K-Medoids Algorithm [9] is shown in figure 7.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

Dimensionality Reduction using Bi-Dimensional Empirical Mode Decomposition Method for Hyperspectral Image Segmentation

Teja*,

Rao

2019

IJRTE

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“…Most of urban and road network remote sensing used image processing methods on color images, like recent approach using clustering algorithm combined with hill climbing for classification of remote sensing image [5]. Another example of current color-based computer vision approach of material identification and classification were used texture-based information [6], [7].…”

Section: Hyper Spectral Imaging Spectroscopy Remote Sensing and Imagmentioning

confidence: 99%

Urban Road Materials Identification using Narrow Near Infrared Vision System

Ipung

Tjandrasa

2017

IJECE

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An urban road materials vision system using narrow band near infrared imaging indexes were proposed. This proposed imaging indexes were enhancement for previous work on autonomous multispectral road sensing method. Each urban road material has different near infrared spectral patterns which is as the base of its spectral identification. The new proposed imaging indexes, which using similar formula of NDVI, was normalized with narrow band near infrared spectrum range of 720nm to 1000nm of wavelength, were used to identify concretes, aggregates/sands/rocks, clay, natural dry fibers and bitumen/asphalt that make up most of urban road materials. This paper proposes imaging indexes evaluation from experiment results to identify those urban road materials. There were seven narrow band optical filter sets with the center spectrum at 710nm, 730nm, 750nm, 800nm, 870nm, 905nm and 970nm. Normalization band used was 720nm using high pass optical filter. The proposed multi-spectral imaging indexes were able to show the potential to classify the selected urban road materials, another approach may need to clearly distinguish between concrete and aggregates. The comparison to the previous imaging indexes (NDVI, NDGR, NDBR) were presented that used for urban road materials identification. Keyword:Imaging index Narrow band optical filter NDBR NDGR NDVI Near infrared vision system Urban road materials

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“…These oscillatory functions are called Intrinsic Mode Functions (IMF), and each IMF satisfies two properties, (a) the number of zero crossings and extrema points should be equal or differ by one. (b) Symmetric envelopes (zero mean) interpret by local maxima and minima [5]. The signal after decomposition using EMD is non-destructive means that the original signal can be obtained by adding the IMFs and residue.…”

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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Clustering Algorithm Combined with Hill Climbing for Classification of Remote Sensing Image

Cited by 20 publications

References 7 publications

Dimensionality Reduction using Bi-Dimensional Empirical Mode Decomposition Method for Hyperspectral Image Segmentation

Dimensionality Reduction using Bi-Dimensional Empirical Mode Decomposition Method for Hyperspectral Image Segmentation

Urban Road Materials Identification using Narrow Near Infrared Vision System

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

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