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This paper examines the utility of hyperspectral imagery for remote sensing data analysis. The acquired data volumes are very important, often reaching hundreds or thousands of channels for a single scene observed. Certainly, the large quantity contained in the hyperspectral database is accompanied by a complex physical content and consequently a considerable time computing which can affect the quality...
The predictive maintenance of industrial machines is one of the challenging applications in the new era of Industry 4.0. Thanks to the predictive capabilities offered by the emerging smart data analytics, data-driven approaches for condition monitoring are becoming widely used for early detection of anomalies on production machines. The aim of this paper is to provide insights on the predictive maintenance...
Land cover classification for remote sensing data have motivated several researches such us source separation, feature extraction and classification method. In this work, we aim to provide enhanced pattern recognition method based on source separation. Then, new data presentation is processed by feature extraction and adaptative classification. The non linearity for the mixing phenomenon is approximted...
Palmprint is a unique and reliable biometric characteristic with high usability. Many works have been carried out on this field, during the past decades. Different algorithms and systems have been proposed and built successfully. Multispectral or hyperspectral palmprint imaging and recognition can be a potential solution to these systems because it can acquire more discriminative information for personal...
Pattern recognition for multispectral data aims to identify land cover thematics for environmental monitoring and disaster risk reduction. Multispectral images contain data acquired from different channels within the frequency spectrum. They represent a mixture of latent signals. This paper represents a pattern recognition contribution for remote sensing. We propose a new classification framework...
In this paper we address the problem of the remote sensing data analysis on high-dimensional space like hyperspectral data. Its complexity is caused by many factors, such as the large spectral or spatial variability and the curse of dimensionality. Much work has been carried out in the literature to overcome this particular ill-posed issue. Applied to hyperspectral data in hyper-dimensional features...
We present a new approach for remote sensing image classification. The methodology combines many related tasks namely non linear source separation, feature extraction, feature fusion and learning classification. Nonlinear source separation is a pre-processing stage that aims to compensate the nonlinear mixing natural phenomenon. Latent signals, called sources are transformed to the feature presentation...
In many geoscience applications, we have to convert remotely sensed images to ground cover maps. Numerous approaches to extract ground cover information have been developed. Recently, blind source separation (BSS) of remote-sensing data has received significant attention due to its suitability to recover sources when no information is available about the scanned zone, hence the term blind. In the...
In this paper, we propose a new preprocessing algorithm for biomedical signals to remove the baseline artifacts. The proposed algorithm is based on the least squares method. It can automatically estimate the form of the baseline artifacts and eliminate it. First, our algorithm consists in using the method of ordinary least squares (OLS) to estimate the slope of linear regression line. Then, we have...
In this paper, we aim to classify remotely sensed images for land characterisation. The major goal is approaching the natural nonlinear mixture for band observation and then dimension reduction by supervised classification. After that, an unsupervised method combining feature extraction and SVM in investigating to discriminate the land cover for SPOT 4 satellite image. In this technique, training...
In this paper, we consider the problem of Blind source separation (BSS) method by taking advantage of the sparse modeling of the hyperspectral images. These images are produced by sensors which provide hundreds of narrow and adjacent spectral bands. The idea behind transform domains is to apply some transformations to illustrate the dataset with a minimum of components and a maximum of essential information...
Source separation is relatively a new area of data analysis. The most widely used separation approach's are linear. However, in many realistic cases the process which generates the observations is nonlinear and no information is available about the mixture. In this case, it can be expected to capture the structure of the data better if the data points lie in a nonlinear manifold instead of a linear...
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