Titlebook: Mathematical Models for Remote Sensing Image Processing; Models and Methods f Gabriele Moser,Josiane Zerubia Book 2018 Springer Internation

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TOXIN

Polarimetric SAR Modelling: Mellin Kind Statistics and Time-Frequency Analysis, transform the polarization state of incoming electromagnetic waves to extract information. This field of remote sensing deals with complex, multi-dimensional data sets, requiring multi-dimensional signal analysis strategies. Target information is in general extracted from PolSAR data by an analysis

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Granular

Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolut variety of data modalities. They encompass multisensor, multiresolution, multiscale, multitemporal, multipolarization, and multifrequency imagery. While they represent remarkable opportunities for the applications, they pose important challenges to the development of mathematical methods aimed at f

Estimable

口味

Satellite Image Time Series: Mathematical Models for Data Mining and Missing Data Restoration,Series (SITS), extremely useful to monitor natural or man-made phenomena on the ground. In this chapter, after providing a brief overview of the most recent methods proposed to process and/or analyze time series of remotely sensed data, we describe methods handling two issues: the unsupervised explo

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Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval,nmental data analysis. Modern imaging sensors offer the necessary spatial and spectral information to tackle a wide range problems through Earth Observation, such as land cover and use updating, urban dynamics, or vegetation and crop monitoring. In the upcoming years even richer information will be