Joint use of color space and wavelet transform for SPOT and RADARSAT image fusion

peer reviewedNous proposons dans cet article une méthode de fusion des images multispectrales et radar par l'utilisation conjointe de l'espace des couleurs et de la transformation en ondelettes. La transformation de l'espace des couleurs permet une séparation des informations spatiale (composante lumineuse) et spectrale (composantes teinte et saturation). La substitution de la composante luminance par une combinaison luminance-radar permet de produire des images fusionnées multispectrale-radar. Celle-ci est produite au moyen d'un processus de fusion appliqué dans le plan des ondelettes pour exploiter les informations contenues dans les images luminance et radar. Pour cela, nous développons un modèle de fusion dépendant des coefficients d'ondelettes et d'un paramètre de fusion qui permet de régler le poids relatif des informations luminance et radar dans le processus de fusion. Un tel paramètre dépend de l'énergie des coefficients qui constitue un bon indicateur de l'importance des coefficients luminance et radar. Pour montrer l'intérêt et l'impact de cette méthode, nous l'évaluons, de façon qualitative et quantitative, sur des images HRV de SPOT et RSO de RADARSAT couvrant une région du Viêt-nam (Haïphong). L'évaluation qualitative révèle que l'information radar est injectée dans les images multispectrales lorsque celle-ci est suffisamment dominante. Cela est mis en évidence par l'examen du paramètre de fusion. Par contre, l'évaluation quantitative, par l'utilisation du coefficient de corrélation et l'écart spectral, montre que les propriétés spectrales sont mieux préservées comparativement à la méthode conventionnelle de substitution de la composante luminance par l'image radar.Usually, two methods are used for using optical and radar images. The first method uses the colour space as the intensity-hue-saturation (IHS) representation for using the multispectral and radar (R) images. It consists in substituting the spatial component (I) by the radar image. Multispectral-radar images are then produced by applying the inverse IHS transform. The second method exploits the wavelet transform (WT) for using panchromatic and radar images by means of a criteria defined in the wavelet domain. The orginality of this work lies in the joint use of the IHS and the wavelet transform for using multispectral and radar images. The development of this method is motivated by the fact that the direct substitution of the I component by the radar image corresponds to rejecting all information coming from the I component. While, this component contains also important information's that should be preserved in the composite image. For this, we propose a more appropriate procedure wich consists in producing a composite I-R component through a combination of the I and R images in order to substitute it to the I component. To ensure that all significant features are selected into I-R, we develop a model, in the wavelet domain, wich allows an adaptative selection of dominant features. This model is a function of I and R wavelet coefficients and a fusion parameter wich allows indicates the relative importance of two components. Such a parameter ranges from zero to one and depends of wavelet coefficient energy. When this parameter tends to zero, the k wavelet coefficient is retained in the I-R comportment. Otherwise, when the parameter tends to one, the I wavelet coefficient is retained. For values of the parameter between these two extreme values, both I and R wavelet coefficients contribute in the I-R comportment. In order to evaluate the interest and the impact of this method, we test it on HRV (XS1, XS2, XS3) images of SPOT and RSO image of RADARSAT covering a region of Viêt-nam (Haïphong Bay). The qualitative evaluation indicates that the radar information is introduced into multispectral images when the details are sufficiently dominant. This is pointed out by examining the values of the fusion parameter through scales wich are represented as images. These constitute a good indicator to point out the relative importance of the I and R information for each region of the images. They may thus be helpful for better interpretation. In order to evaluate, in terms of spectral content, the resemblance of multispectral and fused multispectral-radar images, we use two measures based on correlation coefficient and index deviation. These two measures reveal a better preservation of the spectral properties by our method

The effective use of the DSmT for multi-class classification

International audienceThe extension of the Dezert-Smarandache theory (DSmT) for the multi-class framework has a feasible computational complexity for various applications when the number of classes is limited or reduced typically two classes. In contrast, when the number of classes is large, the DSmT generates a high computational complexity. This paper proposes to investigate the effective use of the DSmT for multi-class classification in conjunction with the Support Vector Machines using the One-Against-All (OAA) implementation, which allows offering two advantages: firstly, it allows modeling the partial ignorance by including the complementary classes in the set of focal elements during the combination process and, secondly, it allows reducing drastically the number of focal elements using a supervised model by introducing exclusive constraints when classes are naturally and mutually exclusive. To illustrate the effective use of the DSmT for multi-class classification, two SVM-OAA implementations are combined according three steps: transformation of the SVM classifier outputs into posterior probabilities using a sigmoid technique of Platt, estimation of masses directly through the proposed model and combination of masses through the Proportional Conflict Redistribution (PCR6). To prove the effective use of the proposed framework, a case study is conducted on the handwritten digit recognition. Experimental results show that it is possible to reduce efficiently both the number of focal elements and the classification error rate

Selective Synthetic Aperture Radar and Panchromatic Image Fusion by Using the àTrous Wavelet Decomposition

Synthetic aperture radar (SAR) imaging sensor presents an important advantage for the earth change observation independently of weather conditions. However, the SAR image provides an incomplete information (as roads) of the observed scene leading thus to an ambiguous interpretation. In order to compensate the lack of features, the high spatial resolution panchromatic (P) image is often used as a complementary data for improving the quality of the SAR image. The concept is based on the extraction of features (details) from the P image in order to incorporate into the SAR image. Therefore, we propose an approach based on the use of the à trous wavelet decomposition (ATWD) for extracting features from the P image. Experimental results show that the SAR-P composite image allows a better detection of lines, edges, and field boundaries

Neural Network Combination by Fuzzy Integral for Robust Change Detection in Remotely Sensed Imagery

Combining multiple neural networks has been used to improve the decision accuracy in many application fields including pattern recognition and classification. In this paper, we investigate the potential of this approach for land cover change detection. In a first step, we perform many experiments in order to find the optimal individual networks in terms of architecture and training rule. In the second step, different neural network change detectors are combined using a method based on the notion of fuzzy integral. This method combines objective evidences in the form of network outputs, with subjective measures of their performances. Various forms of the fuzzy integral, which are, namely, Choquet integral, Sugeno integral, and two extensions of Sugeno integral with ordered weighted averaging operators, are implemented. Experimental analysis using error matrices and Kappa analysis showed that the fuzzy integral outperforms individual networks and constitutes an appropriate strategy to increase the accuracy of change detection.</p

The effective use of the DSmT for multi-class classification

International audienceThe extension of the Dezert-Smarandache theory (DSmT) for the multi-class framework has a feasible computational complexity for various applications when the number of classes is limited or reduced typically two classes. In contrast, when the number of classes is large, the DSmT generates a high computational complexity. This paper proposes to investigate the effective use of the DSmT for multi-class classification in conjunction with the Support Vector Machines using the One-Against-All (OAA) implementation, which allows offering two advantages: firstly, it allows modeling the partial ignorance by including the complementary classes in the set of focal elements during the combination process and, secondly, it allows reducing drastically the number of focal elements using a supervised model by introducing exclusive constraints when classes are naturally and mutually exclusive. To illustrate the effective use of the DSmT for multi-class classification, two SVM-OAA implementations are combined according three steps: transformation of the SVM classifier outputs into posterior probabilities using a sigmoid technique of Platt, estimation of masses directly through the proposed model and combination of masses through the Proportional Conflict Redistribution (PCR6). To prove the effective use of the proposed framework, a case study is conducted on the handwritten digit recognition. Experimental results show that it is possible to reduce efficiently both the number of focal elements and the classification error rate

Swarm intelligence routing approach in networked robots

International audienc