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.
It is observed in remotesensing that a finer spatial resolution does not necessarily improve the classification performance. These observations have been understood by using the conceptual explanation that "boun...
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It is observed in remotesensing that a finer spatial resolution does not necessarily improve the classification performance. These observations have been understood by using the conceptual explanation that "boundary effect" and "within-class variability" work against one another. Though easily understood, this conceptual explanation cannot be readily used for a quantitative investigation. In this study, we design a simulation scheme to evaluate systematically the impacts of various parameters on the classification accuracy. We employ a model for the class spectral covariance of pure pixels and a linear mixing model for the spectral responses of mixed pixels. Based on these models, we derive the statistical characteristics for mixed pixels and assess the corresponding classification errors. As the ratio of ground sampling distance to field size decreases, the classification error associated with pure pixels tends to increase, whereas the classification error associated with mixed pixels tends to decrease from the smaller area of mixed pixels. The simulation results show that the overall classification error first decreases with decreasing ratio of ground sampling distance to field width, reaches a minimum value, and then may increase with further decreasing ratio. Our study on the classification error may help the development of classification schemes for high spatial resolution imagery.
The objective of this work is to identify geological circular forms, impact and volcano craters using satellite images. The recognition of objects (circular forms) is the last step in a processing chain which can be d...
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ISBN:
(纸本)0769513301
The objective of this work is to identify geological circular forms, impact and volcano craters using satellite images. The recognition of objects (circular forms) is the last step in a processing chain which can be described in four phases: imageprocessing, pattern detection, patternrecognition, and identification of the targets (models). The work presents the detection of circular forms in images including the south region of the Minas Gerais State in Brazil.
In the paper, according to characteristics of SST image of satellite remotesensing, we study the detection and recognition of Ocean Meso-scale Eddy. In the research, several methods of imageprocessing are used.
ISBN:
(纸本)0819442763
In the paper, according to characteristics of SST image of satellite remotesensing, we study the detection and recognition of Ocean Meso-scale Eddy. In the research, several methods of imageprocessing are used.
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.
This paper introduces an application of land-cover changes detection based on remotesensingimage analysis. The main purpose of this study is to monitor urban expansions and the changes from cultivate land to constru...
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ISBN:
(纸本)0819442763
This paper introduces an application of land-cover changes detection based on remotesensingimage analysis. The main purpose of this study is to monitor urban expansions and the changes from cultivate land to construction land with multitemporal TM image and SPOT image. Data fusion, image enhancement and texture analysis techniques are used in this case study. The effectiveness of the proposed approach is confirmed by experimental results carried out on remotesensing data of Jinzhou area in Dalian, China.
A description of an approach to primary local imagerecognition is given. The motivation for its application and its characteristics are discussed. Then a method for correction of misclassifications that occur in prim...
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ISBN:
(纸本)081943826X
A description of an approach to primary local imagerecognition is given. The motivation for its application and its characteristics are discussed. Then a method for correction of misclassifications that occur in primary local imagerecognition is proposed. This method uses a graph-based estimation technique that uses information contained in supplementary classes in order to remove misclassifications and/or confirm the correct recognition of pixel hypotheses. In addition, the method is able to remove the supplementary classes after they are no longer needed. The particular features of the considered approach are that it is iterative and uses structures similar to those of center weighted median filters. The numerical simulation results are presented to illustrate the efficiency of the proposed technique.
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