Segmentation of Hyperspectral Images via Subtractive Clustering and Cluster Validation Using One-Class Support Vector Machines

Bilgin, Gökhan; Ertürk, Sarp; Yıldırım, Tülay

doi:10.1109/tgrs.2011.2113186

Cited by 58 publications

(23 citation statements)

References 26 publications

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“…Some of the recent works which have been already done are presented here. Gokham Bilgin, Sarp Erturk and Tulay Yildirim [24] used a new subtractive clustering based on the similarity segmentation and used one-class support vector machine (OC-SVM) as a validation method. Mariam ElTarabily [25] et al proposed a new optimized subtractive clustering technique using the Particle Swarm Optimization technique.…”

Section: Subtractive Algorithmmentioning

confidence: 99%

A Survey on Image Segmentation Methods using Clustering Techniques

Dhanachandra¹,

Chanu²

2017

EJERS

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Image segmentation has been considered as the first step in the image processing. An efficient segmentation result would make it easier for further analysis of image processing. However, there exits many algorithms and approaches for image segmentation. Clustering is one of the commonly used image segmentation techniques. In this paper, we have briefly describe some of the clustering techniques and discuss some of the recent works by researchers on these techniques.

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Section: Subtractive Algorithmmentioning

confidence: 99%

A Survey on Image Segmentation Methods using Clustering Techniques

Dhanachandra¹,

Chanu²

2017

EJERS

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“…Hundreds of spectral components of hyperspectral image pixels can be represented by manifold learning methods in reduced form which then can be useful in applications of feature extraction, visualization, classification [1], [4], segmentation [7], or anomaly detection. Nonlinearities in hyperspectral images based on multiple scattering, bidirectional reflectance distribution function effects, or presence of nonlinear media such as water [8] inspire the employment of nonlinear manifold learning methods.…”

Section: Studies On Hyperspectral Datamentioning

confidence: 99%

“…For linear methods, dimensionality reduction of the rest of the samples was accomplished by simply multiplying data to be transformed with the mapping matrix learned by the corresponding manifold learning algorithm. On the other hand, for nonlinear methods, the same task was realized by the RBF-NN-based interpolation algorithm [7]. Because a direct mapping is not available between the high and the low dimensional spaces for such methods, an RBF-NN was trained using the full and low dimensional training samples as input and output, respectively.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

A comparative study on manifold learning of hyperspectral data for land cover classification

Öztürk

Bilgin

2015

SPIE Proceedings

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This paper focuses on the land cover classification problem by employing a number of manifold learning algorithms in the feature extraction phase, then by running single and ensemble of classifiers in the modeling phase. Manifolds are learned on training samples selected randomly within available data, while the transformation of the remaining test samples is realized for linear and nonlinear methods via the learnt mappings and a radial-basis function neural network based interpolation method, respectively. The classification accuracies of the original data and the embedded manifolds are investigated with several classifiers. Experimental results on a 200-band hyperspectral image indicated that support vector machine was the best classifier for most of the methods, being nearly as accurate as the best classification rate of the original data. Furthermore, our modified version of random subspace classifier could even outperform the classification accuracy of the original data for local Fisher's discriminant analysis method despite of a considerable decrease in the extrinsic dimension.

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“…Their band selection method is global in nature, which means they identified all clusters in commonly reduced set of bands or subspace. Bilgin et al (2011) used a subtractive clusteringbased unsupervised classification of hyperspectral imagery. They also proposed a novel method using one-class support vector machine for cluster validation.…”

Section: Introductionmentioning

confidence: 99%

Comparative study on projected clustering methods for hyperspectral imagery classification

Mehta

Dikshit

2015

Geocarto International

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In this study, projected clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of projected clustering lies in its ability to simultaneously perform feature selection and clustering. This framework also allows selection of different sets of dimensions (features/bands) for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. Experiments are conducted on both synthetic and real hyperspectral imagery. For this purpose, projected clustering algorithms are implemented and compared with k-means and k-means preceded by principal component analysis. Preliminary analyses of studied algorithms on synthetic hyperspectral imagery demonstrate good results. For real hyperspectral imagery, only ORCLUS is able to produce acceptable results as compared to other unsupervised methods. The main concern lies with identification of right parameter settings. More experiments are required in this direction.

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Segmentation of Hyperspectral Images via Subtractive Clustering and Cluster Validation Using One-Class Support Vector Machines

Cited by 58 publications

References 26 publications

A Survey on Image Segmentation Methods using Clustering Techniques

A Survey on Image Segmentation Methods using Clustering Techniques

A comparative study on manifold learning of hyperspectral data for land cover classification

Comparative study on projected clustering methods for hyperspectral imagery classification

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