This note concerns the updating of the land cover map of the Arno River basin (9.000 km) developed in the frame of a research project aimed at landslide risk assessment. The land cover map has been produced through the analysis of panchromatic/multispectral Landsat ETM+ images and TERRA-ASTER data; those data were integrated and compared with B/W and color ortho-photos. To improve photointerpre...

In this paper we propose a novel algorithm for the pansharpening of multispectral images based on the use of a Total Variation (TV) image prior. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images, and uses the sensor characteristics to model the observation process of both panchromatic and multispectral ...

As one kind of remote sensing images of land use composed by various categories of surface objects difficult to obtain multi-distribution model of class spectral feature, analyzing the spectral characteristics of LU of multispectral RS imagery, this paper presents a self-adaptive adjustment of clustering center method. Depending on the intercepted situation of the cluster centers between differ...

The issues connected with the problem of choosing adequate algorithms of the Earth remote sensing multispectral data recognition are considered. The system of supervised classification based on Bayesian strategy of maximum likelihood for normally distributed vectors of features is represented. The system of cluster analysis including the algorithm of K means and clusterization based on the anal...

Multispectral bioluminescence tomography (BLT) attracts increasingly more attention in the area of optical molecular imaging. In this paper, we analyze the properties of the solutions to the regularized and discretized multispectral BLT problems. First, we show the solution existence, uniqueness, and its continuous dependence on the data. Then, we introduce stable numerical schemes and derive e...

High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unmanned aerial vehicles (UAVs) are emerging as lower cost and more flexible means to acquire very high resolution imagery. Miniaturized hyperspectral sensors h...