Geographic Information System (GIS) are often old and so, some geographic elements are not represented. From satellite images and/or aerial images, we can detect cartographic elements to integrate them in the GIS and ...
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ISBN:
(纸本)081943826X
Geographic Information System (GIS) are often old and so, some geographic elements are not represented. From satellite images and/or aerial images, we can detect cartographic elements to integrate them in the GIS and then upgrade it. Making it manually is a very long and tedious work, so computer based methods are needed. This paper presents several specific and automatic or semi-automatic methods to detect and identify several types of cartographic elements. These methods are fast and very efficient. A result evaluation is given to permit a manually correction for the non-confident elements.
We study the application of Competitive Neural Networks (CNN) to the Unsupervised analysis of remotesensing Hyperspectral images. CNN are applied as clustering algorithms at the pixel level. We propose their use for ...
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ISBN:
(纸本)081943826X
We study the application of Competitive Neural Networks (CNN) to the Unsupervised analysis of remotesensing Hyperspectral images. CNN are applied as clustering algorithms at the pixel level. We propose their use for the extraction of endmembers and evaluate them through the error induced by the compression/decompression with the CNN in the supervised classification of the images. We show results with the Self Organizing Map and Neural Gas applied to a well known case study.
In this paper, we present the robust Cascaded RM-filter that be able to remove the mixture of impulsive and multiplicative noise in the remotesensing imaging. The designed filter uses combined R- and M- estimators ca...
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ISBN:
(纸本)0819455202
In this paper, we present the robust Cascaded RM-filter that be able to remove the mixture of impulsive and multiplicative noise in the remotesensing imaging. The designed filter uses combined R- and M- estimators called RM-estimators. The Cascaded RM-filter is the consequential connection of two filters. The first filter employs one of the proposed the RM-KNN (MM-KNN, WM-KNN, ABSTM-KNN or MoodM-KNN) filters to provide the impulsive noise rejection and detail preservation. The second filter uses an M-filter to realize multiplicative noise suppression. We apply the simplest cut, Hampel's three part redescending, Andrew's sine, Turkey biweight, and Bernoulli influence functions in the designed filter. Extensive simulations have demonstrated that the Cascaded RM-filter consistently outperforms other filters by balancing the tradeoff between noise suppression and fine detail preservation. Finally, we have presented the implementation of proposed filter on the DSP TMS320C6701 demonstrating that it potentially provides a real-time solution in the processing of the SAR images.
remotesensingimageprocessing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or ...
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ISBN:
(纸本)9781424456536
remotesensingimageprocessing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remotesensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/imageprocessing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remotesensingimageprocessing.
In this paper, we propose to use a quantitative approach based on LS-SVM to perform estimation of the impact of lossy compression on remotesensingimage compression. Kernel function selection and the model parameters...
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ISBN:
(纸本)9781628415001
In this paper, we propose to use a quantitative approach based on LS-SVM to perform estimation of the impact of lossy compression on remotesensingimage compression. Kernel function selection and the model parameters computation are studied for remotesensingimage classification when LS-SVM analysis model is establish. The experiments show that our LS-SVM model achieves a good performance in remotesensingimage compression analysis. Classification accuracy variation according to compression ratio scales are summarized based on our experiments.
An algorithm for image noise-removal based on local adaptive filtering is proposed in this paper. Three features to use into the local transform-domain filtering are suggested. First, filtering is performed on images ...
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ISBN:
(纸本)081943826X
An algorithm for image noise-removal based on local adaptive filtering is proposed in this paper. Three features to use into the local transform-domain filtering are suggested. First, filtering is performed on images corrupted not only by an additive white noise, but also by image-dependent (e.g. film-grain noise) or multiplicative noises. Second, a number of transforms is used instead of the single one, the resulting estimate is a linear combination of estimates from each of the transforms using local statistics. Third, these transforms are equipped with a varying adaptive window size for which we use the so-called intersection of confidence intervals (ICI) rule. Finally, we combine all the estimates for a pixel from neighboring windows by weighted averaging them. Comparison of the algorithm with known techniques for noise removal from images shows the advantage of the new approach, both quantitatively and visually.
This paper presents an adaptive sparse representation scheme for the remotesensingimage, the geometric structure of which is more complex than that of natural image. The presented scheme includes two main stages whi...
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ISBN:
(纸本)9781424442966
This paper presents an adaptive sparse representation scheme for the remotesensingimage, the geometric structure of which is more complex than that of natural image. The presented scheme includes two main stages which are wavelet transform and adaptive directional filter which is designed based on a binary tree. The construction of the binary tree depends on the image geometric information in frequency domain. Besides the properties of multiscale, multidirectionality and nonredundancy, our scheme has an additional property named adaptation which is a particular characteristic. Experimental results show that, in the sense of signal to noise ratio and visual quality, the performance of the nonlinear approximation of our scheme for remotesensingimage is better than most of the existing sparse representation schemes.
Our aim is to build a digital elevation model (DEM) for the basin of Rega River, a tributary of the Baltic Sea, on a 0.5 x 0.5 m grid. It is based on hand-drawn topographical maps in 1:10,000 scale scanned with 508 dp...
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ISBN:
(纸本)081943826X
Our aim is to build a digital elevation model (DEM) for the basin of Rega River, a tributary of the Baltic Sea, on a 0.5 x 0.5 m grid. It is based on hand-drawn topographical maps in 1:10,000 scale scanned with 508 dpi accuracy. Then a digital terrain model (DTM) results from integration of DEM with remotely sensed data (space and airborne images) and detailed geodata. In this paper, we describe algorithms for noise removal, thinning and continuing contour lines, and interpolation of elevation data used to process the topographical maps.
Synthetic Aperture Radar(SAR) and optical remotesensingimage registration is the prerequisite for image fusion and it is of important theoretical significance and practical value. The image registration methods are ...
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ISBN:
(纸本)9781538663967
Synthetic Aperture Radar(SAR) and optical remotesensingimage registration is the prerequisite for image fusion and it is of important theoretical significance and practical value. The image registration methods are mainly divided into the methods based on feature, the methods based on Gray-scale and others. This article systematically sorts out feature-based optical and SAR remotesensingimage registration techniques, summarizes all types of image registration, points out their advantages and disadvantages and predicts the prospects of their future.
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.
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