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Posterior Expectation of the Total Variation model: Properties and Experiments
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Unsupervised classification of hyperspectral images by using linear unmixing algorithm
 in Proc. IEEE ICIP
, 2009
"... In this paper, we present an unsupervised classification algorithm for hyperspectral images. For reducing the dimension of hyperspectral data, we use a linear unmixing algorithm to extract the endmembers and their abundance maps. Compared to the components obtained by traditional PCAbasedmethod, t ..."
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In this paper, we present an unsupervised classification algorithm for hyperspectral images. For reducing the dimension of hyperspectral data, we use a linear unmixing algorithm to extract the endmembers and their abundance maps. Compared to the components obtained by traditional PCAbasedmethod, the abundancemaps have physical meanings (such as the abundance of vegetation). For determining the number of endmembers contained in an image, we propose an eigenvalue based approach. The validation of this approach on synthetic data shows that this approach provides a robust estimation of the actual number of endmembers. Using the estimated abundance maps of the endmemebers, we perform a preliminary segmentation and use the mean values of the segmented regions as feature for the classification. We then perform Kmeans classifications on the segmented abundance maps with the number of clusters determined by the Krzanowski and Lai’s method. 1.
DecisionBased Fusion for Pansharpening of Remote Sensing Images
"... Abstract—Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. A wide range of pansharpening methods are available, each producing images with different characteristics. To compare the performances and characteristics of different methods, a ..."
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Abstract—Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. A wide range of pansharpening methods are available, each producing images with different characteristics. To compare the performances and characteristics of different methods, a contest was held in 2006 by the IEEE Data Fusion Technical Committee. In this contest, À trous wavelet transformbased pansharpening (AWLP) and Laplacian pyramidbased context adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, we observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to design a method taking advantage of both methods by locally selecting the best one. This adaptive decision fusion is performed based on the local scale of the structure. The interest of the proposed method is verified using both visual and quantitative analyses for different Pléiades data sets. Index Terms—Image fusion, remote sensing. I.
HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON SPECTRAL AND GEOMETRICAL FEATURES
"... In this paper, we propose to integrate geometrical features, such as the characteristic scales of structures, with spectral features for the classification of hyperspectral images. The spectral features which only describe the material of structures can not distinguish objects made by the same mate ..."
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In this paper, we propose to integrate geometrical features, such as the characteristic scales of structures, with spectral features for the classification of hyperspectral images. The spectral features which only describe the material of structures can not distinguish objects made by the same material but with different semantic meanings (such as the roofs of some buildings and the roads). The use of geometrical features is therefore necessary. Moreover, since the dimension of a hyperspectral image is usually very high, we use linear unmixing algorithm to extract the endmemebers and their abundance maps in order to represent compactly the spectral information. Afterwards, with the help of these abundance maps, we propose a method based on topographic map of images to estimate local scales of structures in hyperspectral images. The experiment shows that the geometrical features can improve the classification results, especially for the classes made by the same material but with different semantic meanings. When compared to the traditional contextual features (such as morphological profiles), the local scale feature provides satisfactory results without considerably increasing the feature dimension. 1.
PANSHARPENING WITH A DECISION FUSION BASED ON THE LOCAL SIZE INFORMATION
"... Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. Different pansharpening methods produce images with different characteristics. In the 2006 IEEE Data Fusion Contest, Àtrous Wavelet Transform based pansharpening (AWLP) and Context Ad ..."
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Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. Different pansharpening methods produce images with different characteristics. In the 2006 IEEE Data Fusion Contest, Àtrous Wavelet Transform based pansharpening (AWLP) and Context Adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, it was observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to develop a method which could produce results locally approximately similar to the best method, among the two pansharpening methods. In this paper we propose a method which selects either of the two methods for performing pansharpening on local regions, based upon the size of the objects. The results obtained demonstrate that the proposed method produces images with quantitative results approximately similar to the method which is better among the AWLP and CBD pansharpening methods. 1.
Random Fields of Bounded Variation and Computation of their Variation Intensity
, 2014
"... The main purpose of this paper is to define and characterize random fields of bounded variation, that is random fields with sample paths in the space of functions of bounded variation, and to study their mean total variation. Simple formulas are obtained for the mean total directional variation of r ..."
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The main purpose of this paper is to define and characterize random fields of bounded variation, that is random fields with sample paths in the space of functions of bounded variation, and to study their mean total variation. Simple formulas are obtained for the mean total directional variation of random fields, based on known formulas for the directional variation of deterministic functions. It is also shown that the mean variation of random fields with stationary increments is proportional to the Lebesgue measure, and an expression of the constant of proportionality, called the variation intensity, is established. This expression shows in particular that the variation intensity only depends on the family of twodimensional distributions of the stationary increment random field. When restricting to random sets, the obtained results gives generalizations of wellknown formulas from stochastic geometry and mathematical morphology. The interest of these general results is illustrated by computing the variation intensities of several classical stationary random field and random sets model, namely Gaussian random fields and excursion sets, Poisson shot noises, Boolean models, dead leaves models, and random tessellations.
GEOMETRICAL FEATURES FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION MULTISPECTRAL REMOTESENSING IMAGES
"... In order to extract geometrical features from a multispectral image and derive a classification, an approach based on the topographic map of the image is proposed. For each pixel, the most significant structure containing it is extracted. The classification of this pixel is based on its spectral inf ..."
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In order to extract geometrical features from a multispectral image and derive a classification, an approach based on the topographic map of the image is proposed. For each pixel, the most significant structure containing it is extracted. The classification of this pixel is based on its spectral information and the geometrical features of the corresponding structure (its area and perimeter). The results obtained on multispectral remote sensing images taken by two different sensors show the efficiency of the extracted geometrical features for separating some classes with very similar spectral attributes but of different semantic meanings. 1.
Invariant Texture Indexing Using Topographic Maps
"... This paper introduces a new texture analysis scheme which is invariant to local geometric and radiometric changes. The proposed methodology relies on the topographic map of images, obtained from the connected components of level sets. This morphological tool, providing a multiscale and contrastinv ..."
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This paper introduces a new texture analysis scheme which is invariant to local geometric and radiometric changes. The proposed methodology relies on the topographic map of images, obtained from the connected components of level sets. This morphological tool, providing a multiscale and contrastinvariant representation of images, is shown to be well suited to texture analysis. We first make use of invariant moments to extract geometrical information from the topographic map. This yields features that are invariant to local similarities or local affine transformations. These features are invariant to any local contrast change. We then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases. The obtained experimental results outperform the current state of the art in locally invariant texture analysis.