Remotely sensed images degraded by additive white Gaussian noise (AWGN) have low-level vision, resulting in a poor analysis of their contents. To reduce AWGN, two types denoising strategies, sparse-coding-model-based and deep-neural-network-based (DNN), are commonly utilized, which respective merits drawbacks. For example, the former pursue enjoyable performance with high computational burden, ...

During the past decades, volume of big data available in remote sensing (RS) applications has grown significantly. In addition, a number related to monitoring human activity are being developed based on this kind data. This considerably increased demand for RS processing methods. sense, scientific community is facing challenge how maximize potential that produced fast and efficient way. particu...

We present an approach to simultaneously separate and reconstruct signals from a compressively sensed linear mixture. We assume that the signals have a common sparse representation. The approach combines classical Compressive Sensing (CS) theory with a linear mixing model. Since Blind Source Separation (BSS) from a linear mixture is only possible up to permutation and scaling, factoring out the...

Cover: A convective thunderstorm arrives near the small town of Hombori (Mali) during the AMMA field campaign (August 2005). This region is located in the Sahel, a transition zone between the Sahara and the Sudanian Savannas and stretches across the African continent. In this region, the spatial distribution of soil moisture affects the regional climate system including the spatial distribution...

We consider the problem of foreground and background extraction from compressed-sensed (CS) surveillance video. We propose, for the first time in the literature, a principal component analysis (PCA) approach that computes the low-rank subspace of the background scene directly in the CS domain. Rather than computing the conventional L2-norm-based principal components, which are simply the domina...

Systems that track sensed data trigger alerts based on the evaluation of some condition. In the presence of loss data a conservative condition may not generate a necessary alert and an aggressive condition may generate an alert that could have never happened. We observe that some lost values can be predicted and suggest new classes of conditions that provide more accurate alerts. We motivate th...

different regions [3]. Given a digitized image containing several objects, the image classification process consists of three major phases. AbstractThe analysis of a remote sensing image usually requires the comparison of the analyzed image with another image taken from the same spot. In an automated object identification system for remotely sensed images, thresholding techniques are used to an...

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