Spectral-spatial classification with k-means++ particional clustering

Zimichev, Evgeniy A.; Kazanskiy, Nikolay L.; Serafimovich, P. G.

doi:10.18287/0134-2452-2014-38-2-281-286

Cited by 58 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When clustering incomplete data from congruent sources (e.g. hyperspectral information in Earth remote sensing), this complex can compete with traditional methods such as k-means [20].…”

Section: Resultsmentioning

confidence: 99%

Intelligent Analysis of Incomplete Data for Buildingformalontologies

Semenova

Smirnov

2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

View full text Add to dashboard Cite

Abstract. The article presents models and methods of ontological data analysis aimed at identifying conceptual structure, or formal ontology, of the studied knowledge domain (KD). The realities of the accumulation of empirical data are reflected in model of generalized "object-properties" table and the incompleteness of this information implies the need for models of multi-valued logic. For the first time detailed models and method of accounting for "existence constraints", which can be known priori by the researcher of KD in the context of measured attributes of the objects' learning sample.Keywords: formal ontology, ontological data analysis, formal concept analysis, multi-valued logic, properties existence constraints Citation:Semenova VA, Smirnov SV. Intelligent analysis of incomplete data to building formal ontologies.

show abstract

“…When clustering incomplete data from congruent sources (e.g. hyperspectral information in Earth remote sensing), this complex can compete with traditional methods such as k-means [20].…”

Section: Resultsmentioning

confidence: 99%

Intelligent Analysis of Incomplete Data for Buildingformalontologies

Semenova

Smirnov

2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

View full text Add to dashboard Cite

show abstract

“…In this paper, we used this algorithm with the squared Euclidean distance measure. To initialize cluster centers we used the k-means++ algorithm [17,19]. It was shown that k-means++ algorithm achieves faster convergence to a lower local minimum than the base algorithm.…”

Section: Second and Third Principal Components Contrasted (B-d)mentioning

confidence: 99%

“…There are a growing number of papers that use both unsupervised segmentation and supervised classification techniques to build sophisticated classification methods with improved classification accuracy [5,6,19].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches

Myasnikov¹

2017

Computer Optics

View full text Add to dashboard Cite

Unsupervised segmentation of hyperspectral satellite images is a challenging task due to the nature of such images. In this paper, we address this task using the following three-step procedure. First, we reduce the dimensionality of the hyperspectral images. Then, we apply one of classical segmentation algorithms (segmentation via clustering, region growing, or watershed transform). Finally, to overcome the problem of over-segmentation, we use a region merging procedure based on priority queues. To find the parameters of the algorithms and to compare the segmentation approaches, we use known measures of the segmentation quality (global consistency error and rand index) and well-known hyperspectral images.Keywords: hyperspectral image, segmentation, clustering, watershed transform, region growing, region merging, segmentation quality measure, global consistency error, rand index.Citation: Myasnikov EV. Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches. Computer Optics 2017; 41(4): 564-572. DOI: 10.18287/2412DOI: 10.18287/ -6179-2017.Acknowledgments: The reported study was funded by the Russian Foundation for Basic Research (RFBR) grants 16-29-09494 ofi_m and 16-37-00202 mol_a. IntroductionA hyperspectral image is a three-dimensional array having two spatial dimensions, and one spectral dimension. Every pixel of a hyperspectral image is a vector containing hundreds of components corresponding to a wide range of wavelengths. Compared to grayscale and multispectral images, hyperspectral images offer new opportunities allowing to extract information about materials (components) located on images. Thanks to these unique properties, hyperspectral images are used in agriculture, medicine, chemistry and many other fields.However, high dimensionality of hyperspectral images often makes it impossible to directly apply traditional image analysis techniques to such images. For this reason, hyperspectral image analysis became an extensively studying area last years. In this paper, we consider the segmentation of hyperspectral images, which is one of the most important tasks in hyperspectral image analysis [1 -6]. Other important tasks include, for example, classification [7], detection of anomalies [8], etc.Image segmentation is the process of partitioning an image into connected regions with homogenous properties. In image analysis, segmentation methods are usually divided into three classes [1]: feature-based methods, region-based methods, and edge-based methods.Feature-based methods split all image pixels into subsets, based on their values or derived properties. Thus, first class of methods operates in spectral or derived space. This class includes methods based on clustering [2,3]. Region-based and edge-based methods operate on a spatial domain. Region-based methods use some homogeneity criterion to detect regions in an image. This class includes methods based on region growing, and watershed transformation [4,5]. Edge-based methods use the properties of discont...

show abstract

“…To form the hyperspectral cube [16], the sequence of spectrometer images should be processed. The hyperspectral image of the illuminated with white light graphic table was restored after the processing of the resulting images.…”

Section: Fig 6 the Spectral Image Of The Graphic Table Section Illmentioning

confidence: 99%

Experimental Study of the Optical Transfer Function (Otf) and Spectral Accuracy of the Imaging Hyperspectrometer Based on the Offner Scheme

Blank

Скиданов

2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

View full text Add to dashboard Cite

Abstract. We deal with the imaging spectrometer based on the Offner scheme. We define the optical transfer function of the imaging spectrometer based on the Offner scheme. We define the spectrum reconstruction error for red LED.Keywords: diffraction gratings, imaging hyperspectrometer, spectral image, Offner scheme.Citation: Blank VA, Skidanov RV. Experimental study of the optical transfer function (OTF) and spectral accuracy of the imaging hyperspectrometer based on the Offner scheme.

show abstract

Spectral-spatial classification with k-means++ particional clustering

Cited by 58 publications

References 0 publications

Intelligent Analysis of Incomplete Data for Buildingformalontologies

Intelligent Analysis of Incomplete Data for Buildingformalontologies

Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches

Experimental Study of the Optical Transfer Function (Otf) and Spectral Accuracy of the Imaging Hyperspectrometer Based on the Offner Scheme

Contact Info

Product

Resources

About