In the last decade, the application of statistical and neural network classifiers to remote-sensingimages has been deeply investigated. Therefore, performances, characteristics, and pros and cons of such classifiers ...
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ISBN:
(纸本)081943826X
In the last decade, the application of statistical and neural network classifiers to remote-sensingimages has been deeply investigated. Therefore, performances, characteristics, and pros and cons of such classifiers are quite well known, even from remote-sensing practitioners. In this paper, we present the application to remote-sensingimage classification of a new patternrecognition technique recently introduced within the framework of the Statistical Learning Theory developed by V. Vapnik and his co-workers, namely, the Support Vector Machines (SVMs). In section 1, the main theoretical foundations of SVMs are presented. In section 2, experiments carried out on a data set of multisensor remote-sensingimages are described, with particular emphasis on the design and training phase of a SVM. In section 3, the experimental results are reported, together with a comparison between the performances of SVMs, neural network, and k-NN classifiers.
The objective of this work is to identify geological circular forms, impact and volcano craters, using satellite images. The recognition of objects (circular forms) in the scene is the last step in a processing chain,...
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ISBN:
(纸本)0819439827
The objective of this work is to identify geological circular forms, impact and volcano craters, using satellite images. The recognition of objects (circular forms) in the scene is the last step in a processing chain, which can be described in four phases: image preprocessing, pattern detection, patternrecognition, and identification of the targets (models). The paper presents the detection of circular forms on images including the south region of the Minas Gerais State in Brazil.
Classifier fusion approaches are receiving increasing attention for their capability of improving classification performances. At present, the usual operation mechanism for classifier fusion is the "combination&q...
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ISBN:
(纸本)081943826X
Classifier fusion approaches are receiving increasing attention for their capability of improving classification performances. At present, the usual operation mechanism for classifier fusion is the "combination" of classifier outputs. improvements in performances are related to the degree of "error diversity" among combined classifiers. Unfortunately, in remote-sensingimagerecognition applications, it may be difficult to design an ensemble that exhibit an high degree of error diversity. Recently, some researchers have pointed out the potentialities of "dynamic classifier selection" (DCS) as an alternative operation mechanism. DCS techniques are based on a function that selects the most appropriate classifier for each input pattern. The assumption of uncorrelated errors is not necessary for DCS because an "optimal" classifier selector always selects the most appropriate classifier for each test pattern. The potentialities of DCS have been motivated so far by experimental results on ensemble of classifiers trained using the same feature set, in this paper, we present an approach to multisensor remote-sensingimage classification based on DCS. A selection function is presented aimed at choosing among classifiers created using different feature sets. The experimental results obtained in the classification of remote-sensingimages and comparisons with different combination methods are reported.
This contribution describes a new approach for detection of shadow areas appearing in remotesensingimage data. Identification of objects like streets or vehicles is frequently disturbed by illumination effects like ...
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ISBN:
(纸本)0780367154
This contribution describes a new approach for detection of shadow areas appearing in remotesensingimage data. Identification of objects like streets or vehicles is frequently disturbed by illumination effects like hard shadows or inhomogenous darkening due to varying tilt angles of the processed terrain. To increase the reliability of the recognition process, we apply a sensor fusion of elevation data from laserscanning and optical image data. The represented algorithm improves the results iteratively. The different results are discussed and then used for further processing within a radiometric equalization.
The approach to the detection of fires suggested here is based on methods of patternrecognition in spaces of the informative parameters from information contained in indirect measurements, which in this case are five...
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ISBN:
(纸本)0819438278
The approach to the detection of fires suggested here is based on methods of patternrecognition in spaces of the informative parameters from information contained in indirect measurements, which in this case are five-channel videodata recorded with the AVHRR instrument placed onboard NOAA satellites. A problem of preliminary integrated normalization of satellite videodata, including a transition to constant dimensions of scanning spot projections on the Earth's surface, an increase in the spatial resolution of images for a model of integration within the spot, and correction of the radiobrightness characteristics of the images, is considered. Normalized images are subsequently used to solve the problem of detecting small-sized fires with the help of a three-stage procedure by an algorithm of patternrecognition in space of the informative parameters. A natural criterion for estimating the information content for the class of detection and patternrecognition problems is the functional of the average risk. In this case, the informative set of parameters and the decision rule are found by minimization of this functional. Because conditional probability densities, being mathematical models of stochastic images, are unknown, the problem of reconstructing distributions based on teaching samples with the use of nonparametric estimates with modified Epanechnikov kernel is solved. Unknown parameters of distributions are determined by minimization of a functional of the empirical risk. A comparison between the results of operation by the algorithm and the operator work demonstrates high efficiency of the algorithm of detecting thermal anomalies of fire types.
The aim of the work is To propose a methodology for spatial/spectral analysis of urban patterns using neural network. To address the problem of spectral ambiguity and spatial complexity related to built-up patterns a ...
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ISBN:
(纸本)081943826X
The aim of the work is To propose a methodology for spatial/spectral analysis of urban patterns using neural network. To address the problem of spectral ambiguity and spatial complexity related to built-up patterns a two-stage classification procedure based on Multi-Layer Perceptron, is proposed. The first stage is devoted to generate discriminating features for problematic patterns by a supervised soft classification It uses a moving window to evaluate the neighbouring influences during the classification. The spatial relationships among the window pixels to be classified are not explicitly formalised, but the corresponding window is directly presented as input to the neural network classifier. The generated features are used in the second stage for complete land cover mapping. For an experimental evaluation the strategy has been applied to the classification of natural colour aerial photographs acquired over heterogeneous landscape, including urban patterns, and characterised by high spatial resolution and low spectral information. The proposed methodology for the extraction of urban patterns proved to be accurate and robust besides transferable.
The problem of Map-matching is one key problem in the field of aircraft and vehicle guidance. It deals with the technologies of remotesensing, computer vision, imageprocessing and patternrecognition, etc. Researche...
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ISBN:
(纸本)0819442836
The problem of Map-matching is one key problem in the field of aircraft and vehicle guidance. It deals with the technologies of remotesensing, computer vision, imageprocessing and patternrecognition, etc. Researchers are focusing on how to improve the system's performance, to reduce the searching times and error matching probability [1]. With using an improved quadtree image representative method and the idea of the sequential similarity detection algorithm (SSDA), a hierarchical map-matching algorithm based on embedded MPP system is designed in this paper. The algorithm can greatly reduce matching times and improve locate accuracy.
In the literature, the introduction of the reject option in multiple classifier systems has been analysed only from the experimental point of view. Following a first theoretical analysis provided by the authors, we an...
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In the literature, the introduction of the reject option in multiple classifier systems has been analysed only from the experimental point of view. Following a first theoretical analysis provided by the authors, we analyse, within the framework of the minimum risk theory, the problem of finding the best error-reject trade-off achievable by a linear combination of a given set of trained classifiers. An algorithm for computing the parameters of the linear combination and of the reject rule is then proposed. Experimental results on two data sets of remote-sensingimages are reported.
In this paper the method of image compression is presented. It is designed for data processing in real-time systems of remotesensing. In the midpoint there are compression algorithm based on component transformation ...
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ISBN:
(纸本)0769507514
In this paper the method of image compression is presented. It is designed for data processing in real-time systems of remotesensing. In the midpoint there are compression algorithm based on component transformation with pixel interpolation and algorithm of stabilization of encoded image forming speed, which procine high compression ratio, stable speed of an output data flow and controlled error of image reconstruction.
This paper presents a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. The proposed SFCM classifier can be iterative or non iterative to reduce computational time. Comparison ...
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ISBN:
(纸本)0780362985
This paper presents a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. The proposed SFCM classifier can be iterative or non iterative to reduce computational time. Comparison with the Conventional FCM I clustering technique and the Bayesian classification technique is also presented. Performance results of the three algorithms are presented on simulated and real remotesensing multispectral data, which show improvement in the classification accuracy using the SFCM technique.
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