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2017 international Conference Information Technology and Nanotechnology. Session Image Processing, Geoinformation Technology and Information Security, IPGTIS-ITNT 2017

作者： Choudhary, Komal Boori, M.S. Kupriyanov, A. Samara National Research University 34 Moskovskoe Shosse Samara443086 Russia American Sentinel University 2260 South Xanadu Way AuroraCO80014 United States Image Processing Systems Institute Branch of the Federal Scientific Research Centre Crystallography and Photonics Russian Academy of Sciences 151 Molodogvardeyskaya st. Samara443001 Russia Bonn University Meckenheimer Allee 166 BonnD-53115 Germany

Spatio-temporal analysis is a process for city development with growing population and economy for better implementation of planning policies with advance technology. In this research work, three dates (1995, 2005 & 2016) satellite images were used to mapping and monitoring of Moscow region, Russia. This study focuses on the further classification of the study area into different categories on the basis of use and association by implementing a rule-based classification system on remotely sensed data. This research provides useful and up-to-date information to local land use planners, managers and policy-makers to step up towards sustainable development in Moscow region, Russia.

关键词： Land use

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remote sensing and spatial overlaying analysis based spatially sampling for monitoring urban land change

Remote sensing and spatial overlaying analysis based spatial...

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international workshop on Education Technology and Training/international workshop on Geoscience and remote sensing (ETT and GRS 2008)

作者： Zhang, Xue Tong, Xiao-hua Liu, Miao-long Tongji Univ Dept Surveying & Geoinfo Shanghai 200092 Peoples R China

ISBN: (纸本)9780769535630

In this paper, the remote sensing classification results of urban land of Trading district of Shanghai, China in the years of 1995 and 2001 were employed as expatiated case. The theory of sampling with the integration of remote sensing technique and spatial overlaying analysis for monitoring urban land change has been investigated. The dynamic urban land sampling framework (DULSF) is first investigated, in which three parts were built and implemented that: (1) a dynamic changed urban land regionalization (DCULR) in the case area based on the dynamic urban land change degree (ULCD);(2) a dynamic spatial information and sampling framework (DSISF) based on different urban land change rate;and (3) a dynamic changed urban land sampling units (DCULSU). And a crucial metric that dynamic urban land change degrees of sampling unit (DULCDSU) was proposed in the dynamic urban land sampling framework. A dynamic urban land change sampling (ULCS) scheme was then generated while the valid samples were determined, sample distribution strategy was disposed, and accuracy prior and post assessment was carried out. Finally, the dynamic urban land change sampling data is summarized for case area. Some conclusive remarks are revealed that: (1) the theory of dynamic spatial information and sampling framework in this paper is feasible to control the sampling operation, and (2) the proposed spatially sampling scheme is efficient based on the remote sensing and spatial overlaying analysis.

关键词： remote sensing Spatial overlay analysis Spatially sampling urban land change

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proceedings of the 1995 international workshop on soft computing in remote sensing data analysis

Proceedings of the 1995 International Workshop on Soft Compu...

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proceedings of the 1995 international workshop on soft computing in remote sensing data analysis

proceedings (12 reports) of the Conference on Non-Conventional Pattern analysis in remote sensing are presented. The main topics discussed at the Conference were following: neural networks for geographic information processing;algorithms for supervised classification of remote sensing images;fuzzy logic and neural techniques integration;numeric and symbolic data fusion as an approach to remote sensing image analysis;incorporating mixed pixels in supervised classification development and an approach to fuzzy land cover mapping.

关键词： Image analysis

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soft computing techniques for data classification in a landslide-prone area of Italy

Soft computing techniques for data classification in a lands...

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proceedings of the 1999 IEEE international Geoscience and remote sensing Symposium (IGARSS'99) 'remote sensing of the Systems Earth - A Challenge for the 21st Century'

作者： Blonda, P. Satalino, G. Alberga, V. Wasowski, J. Parise, M. Chiaradia, M.T. Viggiano, R. Pappalepore, M. IESI-CNR Bari Italy

Single and repeat-pass SAR interferometry appears to be a new promising tool for a wide variety of applications. Recent studies have already shown that interferometric correlation, e.g. coherence and intensity images can be coupled to improve thematic land classification. The techniques used to combine and analyze such information are mainly based on thresholding schemes and classical maximum likelihood, although SAR data are not normally distributed. The main objective of this work is the validation of soft computing techniques for the analysis of ERS SAR intensity images combined with interferometric correlation images. A multi-modular neural system, composed by unsupervised and supervised architectures was applied to the analysis of one tandem ERS 1/2 pair, 27-28 August 1995, and one single ERS2 image, 24 July 1995.

关键词： Synthetic aperture radar

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soft computing techniques for data classification in a landslide-prone area of Italy

Soft computing techniques for data classification in a lands...

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IEEE international Symposium on Geoscience and remote sensing (IGARSS)

作者： P. Blonda G. Satalino V. Alberga J. Wasowski M. Parise M.T. Chiaradia R. Viggiano M. Pappalepore IESI/CNR Bari Italy Centro Spaziale di Matera LOC. Nuova Telespazio S.p.A. Matera Italy CERIST CNR Bari Italy Dipartimento Interateneo di Fisica I.N.F.M Bari Italy Centro Spaziale di Matera Nuova Telespazio S.p.A. Matera Italy Tecnopolis Plsnetek Italia s.r.l Valenzano Bari Italy

Single and repeat-pass SAR interferometry appears to be a new promising tool for a wide variety of applications. Recent studies have already shown that interferometric correlation, e.g. coherence and intensity images can be coupled to improve thematic land classification. The techniques used to combine and analyze such information are mainly based on thresholding schemes and classical maximum likelihood, although SAR data are not normally distributed. The main objective of this work is the validation of soft computing techniques for the analysis of ERS SAR intensity images combined with interferometric correlation images. A multi-modular neural system, composed by unsupervised and supervised architectures was applied to the analysis of one tandem ERS 1/2, pair, 27-28 August 1995, and one single ERS 2 image, 24 July 1995.

关键词： Terrain factors Principal component analysis Image analysis Computer architecture Neural networks Computational efficiency Lab-on-a-chip Interferometry Information analysis data mining

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Numeric and symbolic data fusion: A soft computing approach to remote sensing images analysis

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PATTERN RECOGNITION LETTERS 1996年第13期17卷 1361-1378页

作者： Desachy, J Roux, L Zahzah, EH UNIV TOULOUSE 3 IRIT F-31062 TOULOUSE FRANCE UNIV LA ROCHELLE L3I F-17000 LA ROCHELLE FRANCE

An expert system approach for image classification according to expert knowledge about best sites for vegetation classes is described. Uncertainty management is solved by a certainty factor approach. The numerical and symbolic data fusion is viewed as an updating process. The fusion approach is then described. A neural classifier applied to image data is the first source. A set of fuzzy neural networks representing expert knowledge constitutes the second source. A conjunctive combination based on evidence theory is applied. Finally, a possibility theory-based pooling aggregation rule is presented. These three approaches are applied to a vegetation classification problem.

关键词： satellite image classification information fusion neural networks evidence and possibility theory

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An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images

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PATTERN RECOGNITION LETTERS 1996年第13期17卷 1331-1341页

作者： Serpico, SB Bruzzone, L Roli, F UNIV CAGLIARI DEPT ELECT & ELECT ENGNI-09123 CAGLIARIITALY

An experimental analysis of the use of different neural models for the supervised classification of multisensor remote-sensing data is presented. Three types of neural classifiers are considered: the Multilayer Perceptron, a kind of Structured Neural Network, proposed by the authors, that allows the interpretation of the network operation, and a Probabilistic Neural Network. Furthermore, the k-nearest neighbour statistical classifier is also considered in order to evaluate the validity of the aforementioned neural networks, as compared with that of classical statistical methods. The results provided by the above classifiers are compared.

关键词： artificial neural networks supervised classifiers remote-sensing image classification

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A hybrid approach to fuzzy land cover mapping

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PATTERN RECOGNITION LETTERS 1996年第13期17卷 1399-1410页

作者： Binaghi, E Brivio, PA Ghezzi, P Rampini, A Zilioli, E CNR TELERILEVAMENTO IRRSI-20131 MILANITALY

We propose here a fuzzy hybrid methodology for the classification, conceived as a cognitive process, of remote sensing images. The salient aspect of the approach is the combined use of different techniques: the linear mixture model, a supervised fuzzy statistical classifier and a fuzzy labeling technique, An application for the identification of rice crops in a Landsat Thematic Mapper image has been developed with the aim of experimentally evaluating the performance of the overall strategy in a real domain where fuzzy membership to classes are essential in class discrimination, The results have then been compared with those obtained by means of the Maximum Likelihood classifier.

关键词： remote sensing image classification fuzzy supervised classification linear mixture model fuzzy labeling

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Fuzzy logic and neural techniques integration: An application to remotely sensed data

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PATTERN RECOGNITION LETTERS 1996年第13期17卷 1343-1348页

作者： Blonda, P Bennardo, A Satalino, G Pasquariello, G Istituto Elaborazione Segnali ed Immagini I.E.S.I-C.N.R. Via Amendola 166/5 70126 Bari Italy

The paper reviews the most recent proposals on the integration of fuzzy and neural networks techniques. First, it focuses on the strategies developed and employed for the fuzzification of neural network architectures. Then it applies an unsupervised fuzzy architecture to the analysis of remotely sensed data and compares the results with those obtained by means of a conventional neural model.

关键词： neuro-fuzzy fuzzy logic neural networks remotely sensed data classification

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Active fusion - A new method applied to remote sensing image interpretation

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PATTERN RECOGNITION LETTERS 1996年第13期17卷 1349-1359页

作者： Pinz, A Prantl, M Ganster, H KoppBorotschnig, H Institute for Computer Graphics Technical University Graz A-8010 Graz Austria

Today's computer vision applications often have to deal with multiple, uncertain and incomplete visual information. In this paper, we introduce a new method, termed ''active fusion'', which provides a common framework for active selection and combination of information from multiple sources in order to arrive at a reliable result at reasonable costs. The implementation of active fusion on the basis of probability theory, the Dempster-Shafer theory of evidence and fuzzy sets is discussed. In a sample experiment, active fusion using Bayesian networks is applied to agricultural field classification from multitemporal Landsat imagery. This experiment shows a significant reduction of the number of information sources required for a reliable decision.

关键词： information fusion image understanding active fusion probability theory Bayesian networks Dempster-Shafer theory of evidence fuzzy sets fuzzy measures entropy

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