A comparison of algorithms for supervised classification using hyperspectral data

Kuznetsov, Alexander V.; Myasnikov, Vladislav

doi:10.18287/0134-2452-2014-38-3-494-502

Cited by 34 publications

(11 citation statements)

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“…Using the method of regression tree [16], classifiers were constructed for each polygons, which make it possible to extract territories with similar characteristics of soil cover in space images. As attributes for the classifier, brightness, red, green, blue, and near infrared spectral channels were used; the normalized difference The vegetative index (NDVI) based on NIR and G channels [17], the chlorophyll coefficient [18], the local average in the 3 × 3 window, variance, correlation coefficients and entropy, textural signs of Haralick [19] and Gabor [20].…”

Section: Methodsmentioning

confidence: 99%

Concerning the possibilities of successional changes revealing in anthropogenically transformed ecosystems on the base of remote sensing and ground-based survey data integration

Kavelenova¹,

Prokhorova²,

Корчиков³

et al. 2017

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

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The districts of Samara region are characterized by specific combination of orographic structure, hydrological regimes, soil and vegetation cover features, combined with a high level of anthropogenic pressure. The revealing of negative changes associated with the anthropogenic exploitation regimes incliding salinization and waterlogging after irrigation, soil erosion, transformation of non-cultivated fields into deposits, overgrowing of old quarries etc. seems to be a difficult task when carrying out by ground-based studies related to a large-scale land resources of the region. The use of remote sensing data, resulted by a time series of images for the same territory, opens up wide opportunities, on condition that the regional ground-based standardization is carried out.

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Section: Methodsmentioning

confidence: 99%

Concerning the possibilities of successional changes revealing in anthropogenically transformed ecosystems on the base of remote sensing and ground-based survey data integration

Kavelenova¹,

Prokhorova²,

Корчиков³

et al. 2017

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

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show abstract

“…This allows us to associate the proposed approach with featureless pattern recognition techniques [8,9]. The described method of the data description via the base library functions is applied for archival data function description, as well as for current library function description.…”

Section: The Description Dimensionality Reduction Methodsmentioning

confidence: 99%

Similarity Search Over Program Code Sequences Using Featureless Pattern Recognition Techniques

Yumaganov

Myasnikov

2016

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

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Abstract. In this paper, we describe a problem of searching similar code sequences over binary executable program files. A proposed method is based on function opcode partitioning into functional groups, forming the function descriptions by comparing the executable code with the code of "base" library functions, and the dimensionality reduction of the resulting indirect description, which is used to run a search at the final step. We conducted an experimental study to show, that the proposed method is applicable for the problem solution, and to demonstrate its operability and efficiency.

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Section: Introductionmentioning

confidence: 99%

“…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.…”

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confidence: 99%

Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches

Myasnikov¹

2017

Computer Optics

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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...

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A comparison of algorithms for supervised classification using hyperspectral data

Cited by 34 publications

References 0 publications

Concerning the possibilities of successional changes revealing in anthropogenically transformed ecosystems on the base of remote sensing and ground-based survey data integration

Concerning the possibilities of successional changes revealing in anthropogenically transformed ecosystems on the base of remote sensing and ground-based survey data integration

Similarity Search Over Program Code Sequences Using Featureless Pattern Recognition Techniques

Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches

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