Introduction to the Issue on Advances in Remote Sensing Image Processing

The statistical characterization of remote sensing images turns out to be more difficult than in grayscale natural images because of the pixel’s higher dimensionality, particular noise and uncertainty sources, the high spatial and spectral redundancy, and their inherently nonlinear nature. It is worth noting that all these problems can be addressed in different ways depending on the sensor and the acquisition process. Consequently, the methods for the analysis and processing of remote sensing images need to be carefully designed attending to these needs. Different problems are posed from a signal processing and machine learning point of view: the acquired signals have to be processed in a timely manner, transmitted, further corrected from different distortions, eventually compressed, and ultimately analyzed to extract valuable information from them with, for instance, advanced classification or regression methods. Recently, new learning paradigms have been introduced and the latest advances in signal and image processing tools have been incorporated to the current toolbox of the remote sensing data users

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