Tarek Habib

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Satellite imagery classification using the support vector machine (SVM) algorithm may be a time-consuming task. This may lead to unacceptable performances for risk management applications that are very time constrained. Hence, methods for accelerating the SVM classification are mandatory. From the SVM decision function, it can be noted that the(More)
Due to their flexibility, and capacity to handle high dimensional vectorial data, support vector machines (SVMs) have become the reference for remote sensing imagery classification. However when processing large amounts of data the SVM classification could be a time consuming process. In this paper a new decomposition scheme of the SVM decision function is(More)
High resolution satellite imagery are widely used in many applications. One of the main applications is planet monitoring for hazard assessment. The objective of our study is to develop segmentation methods of spaceborne remote sensing images capable of detecting lakes over large areas in high mountainous regions. In this context three different methods are(More)
In the framework of the International Charter "Space and Major Disasters", charter calls are made to the signing parties every time a natural or technological hazard occurs. Consequently, space data are provided by the partners in order to help local authorities to assess the damages, organize and optimize the use of available resources. In such cases,(More)
In the context of change detection and due to the multitude of change scenarios, the objective is to build a generic change detection system. For many technical and operational reasons the Support Vector Machines (SVM) algorithm is used. One of the crucial steps when using the SVM algorithm is the choice of the kernel function. With the lack of a priori(More)
In the case of single-channel ANC systems, the reference signal is picked up by a reference microphone, and the "antinoise" is generated by a canceling loudspeaker. This antinoise signal propagates downstream to cancel the unwanted noise. However it also propagates upstream and corrupts the reference signal. This is called acoustic feedback. The presence of(More)
The problem of focusing on the most relevant information in a potentially overwhelming quantity of data has become increasingly important. Using irrelevant or noisy features not only can affect the accuracy of the classification results obtained but also the convergence time. In this paper several feature selection algorithms used with the Support Vector(More)
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