作者：
Garcia-Consuegra, J.Cisneros, G.Martinez, A.
Castilla-La Mancha University Campus Universitario s/n 02071 Albacete Spain Grupo de Tratamiento de Imágenes
Universidad Politécnica de Madrid Ciudad Universitaria 28040 Madrid Spain
Castilla-La Mancha University Campus Universitario s/n 02071 Albacete Spain
In this paper we provide a solution to a common problem in remotesensing when woody crops (almond and olive fields, vineyards, and so on) must be located and discriminated. Experience has taught us that, currently, t...
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Thermal hyperspectral imagery introduces new possibilities in remotesensing. This paper deals with testing the accuracy of the supervised classification of the artificial objects in the thermal hyperspectral imagery,...
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As a data structure, a tree is an optimal presentation of hierarchical objects. Many irregular and dynamical phenomena studied for example in biology, medical sciences, meteorology, and geomorphology can be modelled a...
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The AMOVIP project is a joint initiative for a cooperation in specific aspects of vision research and their applications, involving EU partners in Spain and Portugal together with developing countries Mexico and Brazi...
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Mathematical morphology coupled with creation of a time stack image and principal oscillation pattern analysis are used to determine the water depths over a known sloping bottom from synthetic remotely sensed images. ...
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This paper describes the use of a complex modular imageprocessing system for texture classification. An introduction into problems that arise when handling textures is given. Furthermore the modules of the proposed s...
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This paper describes the use of a complex modular imageprocessing system for texture classification. An introduction into problems that arise when handling textures is given. Furthermore the modules of the proposed system are described, namely the filtering and statistical modules, automatic feature vector optimization module and the classification module using clustering and fuzzy clustering methods. This texture classification system can easily be adapted for other tasks, including tasks in the field of medical imaging, remotesensing and quality control.
Segmentation of textured images is becoming more and more important in applications, as quality control or remotesensing. Segmentation of textured images is demanding. A new genetic algorithm based method to post-pro...
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ISBN:
(纸本)0818685123
Segmentation of textured images is becoming more and more important in applications, as quality control or remotesensing. Segmentation of textured images is demanding. A new genetic algorithm based method to post-process segmented texture images is presented. A genetic algorithm is used to extract web-like rules from segmented texture images. These rules are checked and they are used in post-processing to improve the segmentation. An unsupervised image segmentation and definition of classes by class prototypes are assumed. Some preliminary results are presented.
Recent research has demonstrated that a backpropagation neural network classifier is a useful tool for multispectral remotesensingimage classification. However, its training time is too long and the network's ge...
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Recent research has demonstrated that a backpropagation neural network classifier is a useful tool for multispectral remotesensingimage classification. However, its training time is too long and the network's generalization ability is not good enough. Here, a new method is developed not only to accelerate the training speed but also to increase the accuracy of the classification. The method is composed of two steps. First, a simple penal term is added to the conventional squared error to increase the network's generalization ability. Secondly, the fixed factor method is used to find the optimal learning rate. We have applied it to the classification of landsat MSS data. The results show that the training time is much shorter and the accuracy of classification is increased as well. The results are also compared to the maximum likelihood method which demonstrate that the back-propagation neural network classifier is more efficient.
Segmentation of textured images is becoming more and more important in applications, as quality control or remotesensing. Segmentation of textured images is demanding. A new genetic algorithm based method to post-pro...
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Segmentation of textured images is becoming more and more important in applications, as quality control or remotesensing. Segmentation of textured images is demanding. A new genetic algorithm based method to post-process segmented texture images is presented. A genetic algorithm is used to extract web-like rules from segmented texture images. These rules are checked and they are used in post-processing to improve the segmentation. An unsupervised image segmentation and definition of classes by class prototypes are assumed. Some preliminary results are presented.
作者：
Schwarz, GDatcu, MDLR
German Remote Sensing Data Ctr German Aerosp Res Estab DFD D-82234 Oberpfaffenhofen Germany
During the last years, wavelets have become very popular in the fields of signal processing and patternrecognition and have led to a large number of publications. In the discipline of remotesensing several applicati...
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ISBN:
(纸本)0819426490
During the last years, wavelets have become very popular in the fields of signal processing and patternrecognition and have led to a large number of publications. In the discipline of remotesensing several applications of wavelets have emerged, too. Among them are such diverse topics as image data compression, image enhancement, feature extraction, and detailed data analysis. On the other hand, the processing of remotesensingimage data-both for optical and radar data-follows a well-known systematic sequence of correction and data management steps supplemented by dedicated image enhancement and data analysis activities. In the following we will demonstrate where wavelets and wavelet transformed data can be used advantageously within the standard processing chain usually applied to remotesensingimage data. Summarizing potential wavelet applications for remotesensingimage data, we conclude that wavelets offer a variety of new perspectives especially for image coding, analysis, classification, archiving, and enhancement. However, applications requiring geometrical corrections and separate dedicated representation bases will probably remain a stronghold of classical image domain processing techniques.
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