There has been greatly increased activity in the last twelve years on the use of information processing techniques on remotesensing problems including signal/imageprocessing, compression, segmentation, feature extra...
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ISBN:
(纸本)0780338375
There has been greatly increased activity in the last twelve years on the use of information processing techniques on remotesensing problems including signal/imageprocessing, compression, segmentation, feature extraction, patternrecognition, neural networks, etc. The past progress is reviewed from which a trend is developed. The trend shows a further emphasis on using neural networks and wavelet transforms for remotesensing.
Interpolation and classification are widely used in imageprocessing and patternrecognition, including remote sensed imageprocessing. In fact, interpolation and classification can be considered as problems of optimi...
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ISBN:
(纸本)0819426490
Interpolation and classification are widely used in imageprocessing and patternrecognition, including remote sensed imageprocessing. In fact, interpolation and classification can be considered as problems of optimisation, i.e., finding an optimal mapping from the value space of variables to the value space of the function, or from the feature space to the class space. Different methods are used, some are based on known numerical data, and the others, on expert rules. In general, they have difficulty to integrate both the knowledge of experts and that implied in known numerical training samples. In the present paper, we propose to use neural fuzzy systems with asymmetric pi membership functions. A new global criterion to optimise and the corresponding learning algorithm are proposed also. To test the performance of the system proposed we apply it to interpolation and classification problems. The comparison with other methods shows better behavior of such systems. The neural fuzzy system using asymmetric pi membership functions has the following advantages : 1) Asymmetric pi membership function gives a more general model of fuzzy rules, improving the precision of neural fuzzy system and assuring a good convergence in learning. 2) The neural fuzzy system can integrate both kinds of knowledges. 3) The neural fuzzy system allows a refinement of the expert knowledge, and the new fuzzy rules found are easy to interprete.
In this paper we propose a novel method for the robust classification of blurred and noisy images that incorporates ideas from data fusion. The technique is applicable to blind situations in which the exact blurring f...
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In this paper we propose a novel method for the robust classification of blurred and noisy images that incorporates ideas from data fusion. The technique is applicable to blind situations in which the exact blurring function is unknown. The approach treats differently deblurred versions of the same image as distinct correlated sensor readings of the same scene. The images are fused during the classification process to provide a more reliable result. We show analytically that the various restorations can be treated as images acquired from different but correlated sensor readings. Experimental results demonstrate the potential of the method for robust classification of imagery.
Satellite imagery of weather patterns and meteorological information are used to develop a Knowledge Based Weather imageprocessing and classification System (KB/WIS). Wavelet and fractal methods are used to extract f...
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Satellite imagery of weather patterns and meteorological information are used to develop a Knowledge Based Weather imageprocessing and classification System (KB/WIS). Wavelet and fractal methods are used to extract features from weather images. The features extracted are used to represent various weather patterns. The system is statistically trained to characterize and interpret weather patterns. The system is a composite of four components: image acquisition, image pre-processing and enhancement, feature extraction and selection, and weather inference engine. Complete architecture of the KB/WIS system including its applications is described.
Clustering is commonly used in remotesensingimage segmentation. Among clustering techniques, pyramid-based methods generally provide better performance in discriminating among different cover classes if compared to ...
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ISBN:
(纸本)0818681837
Clustering is commonly used in remotesensingimage segmentation. Among clustering techniques, pyramid-based methods generally provide better performance in discriminating among different cover classes if compared to global algorithms. When applied to single polarization Synthetic Aperture Radar (SAR) data, though, such algorithms suffer from misinterpretation problems due to the mono-band nature of the images produced by these sensors. In this case an important feature to improve segmentation is texture: this paper describes a wavelet-based fuzzy clustering algorithm which receives as input both the remotely sensed image and a texture image based on a fractal model, derived from the wavelet representation itself The algorithm has been tested on X-SAR images, and the results demonstrate its potential usefulness.
In this article we report on aspects of the use of colour coded Pseudo Random Binary Arrays (PRBA's) to model three dimensional scenes. We will evaluate the accuracy and the robustness of the patternrecognition p...
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ISBN:
(纸本)0780337484
In this article we report on aspects of the use of colour coded Pseudo Random Binary Arrays (PRBA's) to model three dimensional scenes. We will evaluate the accuracy and the robustness of the patternrecognition phase in the process. Emphasis will be on the added value of the use of PRBA's as a tool to make the imageprocessing robust and highly noise insensitive.
Recently, the concept of "Multiple Classifier Systems" was proposed as a new approach to the development of high performance image classification systems. Multiple Classifier Systems can be used to improve c...
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In this paper we address a new approach to the remotesensing imaging problems for radar/sonar array imaging system stated and treated as ill-posed inverse problems of restoration the extended object reflected signals...
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ISBN:
(纸本)0819424838
In this paper we address a new approach to the remotesensing imaging problems for radar/sonar array imaging system stated and treated as ill-posed inverse problems of restoration the extended object reflected signals distorted in a stochastic scattering medium. The developed approach is based on combining the Bayesian estimation technique for signal restoration problems with constrained regularization technique for inversion of the signal formation operator of the stochastic measurement channel. To reduce the generic ill-posed imaging problem to its radar/sonar system oriented numerical version the experiment design methodology is applied. This results in the projection-dependent scheme for measured data that originates from limited number of sensors of a sparse array. Next, to alleviate the limitations on the absence of prior knowledge of the object signal the model-based assumptions for the desired image space are introduced. Model-based fusion of such diverse information on data sets and image space in a generalized constrained array imaging inverse problem is the first issue addressed in the paper. Optimal/suboptimal solution of this problem in the mixed Bayesian-regularization setting that results in the development of numerical technique for extended object imaging in scattering random media with improved spatial resolution is the second issue this paper addresses. Some computer simulation results are also provided to illustrate the proposed approach.
The papers submitted to the Sixth International conference on imageprocessing and Its Applications are presented. The issues considered include shape description and recognition;imageprocessing applications;texture;...
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The papers submitted to the Sixth International conference on imageprocessing and Its Applications are presented. The issues considered include shape description and recognition;imageprocessing applications;texture;image segmentation;neural networks;colour;inspection and document processing;filtering and morphology;medical applications;transport, security and remotesensing.
The European Concerted Action "COMPARES" (Concerted Action on COnnectionist Methods for Preprocessing and Analysis of remotesensing Data) was hmded within the Environment and Climate Programme of the Europe...
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