Impulse response identification for remote sensing images using gis data

Denisova, Anna; Sergeyev, V. V.

doi:10.18287/0134-2452-2015-39-4-557-563

Cited by 12 publications

(5 citation statements)

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“…Thus, statistical methods can be used to identify FCM parameters alongside with the expert methods. The possibility of using this or that method is determined by the nature of the available statistical information [6]. For instance, if statistical data about concepts are represented in the form of spatial sampling, a linear regression model can be applied to identify the sign and intensity of influence between the concepts.…”

Section: The Present State Of Research In the Field Of Fuzzy Cognitivmentioning

confidence: 99%

Application of time series analysis for structural and parametric identification of fuzzy cognitive models

Isaev¹,

Podvesovskii²

2018

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

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The article deals with problems of structural and parametric identification of fuzzy cognitive models on the basis of statistical data analysis. The feasibility of application of time series analysis for solving these problems is justified. The Granger causality test is proposed for structural identification. An approach for parametric identification based on distributed-lag time series model is also proposed. The results of experimental verification of the described approaches are presented.

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Section: The Present State Of Research In the Field Of Fuzzy Cognitivmentioning

confidence: 99%

Application of time series analysis for structural and parametric identification of fuzzy cognitive models

Isaev¹,

Podvesovskii²

2018

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

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“…It is necessary to develop a database that allows to effectively manage all necessary information for storage of spatially temporary data. A lot of works are devoted to the creation of such databases [18][19][20][21][22][23][24][25]. For this, both specialized structures and relational databases can be used [26][27].…”

Section: Temporal Databasementioning

confidence: 99%

Using spatial-temporal maps for visualization of the karst development dynamics

Sharapov¹

2018

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

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There is a problem of visualization dynamics of karst processes during territory monitoring. For this can be used geographic information system. All information on existing forms of karst processes can connected to the map based on the coordinates. But there is a need to include into GIS the information about the dynamics of the karst processes. There is a big problem to solve this task in traditional GIS. Development of karst processes has features that affect their visualization in GIS: the shape and the state of karst objects may change; existing karst forms can be replaced with new objects; the frequency of state changes and appearance of new objects is small; cartographic basis (map) may change as a result of the impact of natural phenomena and human activities. To solve the problem can be used spatial-temporal GIS. In paper the basic features of the karst processes development that affect their storage and display on maps are given. The questions of organization of karst processes dynamics visualization on the basis of spatial-temporal maps are presented. Practical issues of linking the objects in map, display of them geometry, determine temporal boundaries of objects existence are describes.

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“…Occurrence of geoinformation systems (GIS) and electronic map services makes possible to use accumulated vector data for remote sensing image processing. In article [7] we proposed a modification of energy spectrum method using GIS data. This modification was considered in case of exact geometrical matching of observed image and vector map.…”

Section: Information Technology and Nanotechnology (Itnt-2016) 297mentioning

confidence: 99%

Using Gis Data to Identify Linear Observation Model on Remote Sensing Images in Case of Spatial Mismatch of Input Image and Vector Map

Denisova

Sergeyev

2016

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

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Abstract. This paper presents an experimental research of identification method proposed by the authors in their previous works. The method was developed to identify a linear observation model in remote sensing images. It uses the relation between energy spectra of input and output images, and it is called energy spectrum method. The latest modification of the method applies vector map data from geoinforation system to construct an image with energy spectrum similar to energy spectrum of unknown undistorted image. Vector map and input image had been supposed to be exactly spatially coherent. In this paper the algorithm's quality is investigated in case of inexact spatial matching of input image and vector map. Accuracy of input image georeference and accuracy of vector map borders are considered as major spatial mismatch factors. Experimental evaluation of the these factors influence on quality of impulse response restoration is provided. IntroductionThe common way to describe the process of image acquisition in remote sensing systems is a linear observation model [1]. For different applications, for example, for image correction, it is important to estimate the parameters of the model using only observed image. State of the art methods of system's impulse response identification [2-4] work mainly with one dimensional signals or have high computational complexity, that significantly impedes their use in case of remote sensing images.

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Impulse response identification for remote sensing images using gis data

Cited by 12 publications

References 0 publications

Application of time series analysis for structural and parametric identification of fuzzy cognitive models

Application of time series analysis for structural and parametric identification of fuzzy cognitive models

Using spatial-temporal maps for visualization of the karst development dynamics

Using Gis Data to Identify Linear Observation Model on Remote Sensing Images in Case of Spatial Mismatch of Input Image and Vector Map

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