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.
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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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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By propagating a vector for each pixel, we show that nearly Euclidean distance maps can be produced quickly by a region growing algorithm using hierarchical queues. Properties of the propagation scheme are used to det...
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We address in this paper the problem of defining fuzzy relationships between objects for patternrecognition purposes. We distinguish two kinds of spatial relationships. Some of them are well defined if the objects ar...
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ISBN:
(纸本)0819423599
We address in this paper the problem of defining fuzzy relationships between objects for patternrecognition purposes. We distinguish two kinds of spatial relationships. Some of them are well defined if the objects are crisp, like adjacency, inclusion or other set relationships. But since they are highly sensitive to errors or imprecision in segmentation, more useful measures can be obtained by fuzzifying these concepts. We define such measures using fuzzification principles or direct translation of binary equations into fuzzy ones. Other relationships are inherently vague concepts, like relative position. Fuzzy definitions of such relationships are then more consistent than crisp ones. We propose definitions of relative position between crisp or fuzzy objects, and illustrate them on buildings in aerial images.
Proceedings (12 reports) of the conference on Non-Conventional pattern Analysis in remotesensing are presented. The main topics discussed at the conference were following: neural networks for geographic information p...
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Proceedings (12 reports) of the conference on Non-Conventional pattern Analysis in remotesensing are presented. The main topics discussed at the conference were following: neural networks for geographic information processing;algorithms for supervised classification of remotesensingimages;fuzzy logic and neural techniques integration;numeric and symbolic data fusion as an approach to remotesensingimage analysis;incorporating mixed pixels in supervised classification development and an approach to fuzzy land cover mapping.
In the framework of an image interpretation system for automatic cartography based on remotesensingimage classification improved by a photo interpreter knowledge, we developed a system based on neural networks which...
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ISBN:
(纸本)0819423599
In the framework of an image interpretation system for automatic cartography based on remotesensingimage classification improved by a photo interpreter knowledge, we developed a system based on neural networks which simultaneously produce fuzzy rules, with their linguistic approximation as well as final classification. This paper describes the succession of steps used with this aim in view. Particularly it investigates the application of mutual information criteria to simplify fuzzy rules.
The scattering process of electromagnetic waves is dominated by the match between wavelength and the geometric dimensions of surface structures. With respect to the microwave radar bands millimeter-waves are better ma...
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ISBN:
(纸本)0819421235
The scattering process of electromagnetic waves is dominated by the match between wavelength and the geometric dimensions of surface structures. With respect to the microwave radar bands millimeter-waves are better matched to small surface features of terrain. Therefore this frequency band is able to gain additional information on the terrain of interest. For high resolution imaging SAR is the favorite solution also for millimeter-wave frequencies. Compared to more classical radar bands millimeter-waves offer advantages in the SAR processing, because due to the higher primary resolution at a given antenna aperture sources of image distortions such as range migration or depth of focus can be neglected at these frequencies. Moreover the inherently short aperture time for a given resolution improves the relation to the time constant of flight instabilities and makes motion compensation a simple process. A coherent, polarimetric, high range resolution radar, operating at a nominal frequency of 94 GHz, has been installed onboard an aircraft to allow remotesensing measurements in a side looking synthetic aperture approach. The radar-raw-data were registered together with time code and inertial data of the aircraft and later on evaluated by an off-line SAR-processor. The resulting images then had to undergo an automatic recognition process to extract certain complex targets using a knowledge based production system. The paper describes the measurement system and discusses the evaluation procedures with emphasis on the applied SAR algorithm. Examples of radar images at 94 GHz are shown and samples of patternrecognition derived from the SAR images are shown.
imageprocessing applications and especially those in the area of remotesensing are often characterized by a high degree of complexity. We introduce a general framework, called 'active fusion', that actively ...
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