In this section, we provide the essential principles of deep learning. After reading this chapter, one should have the required theoretical basis to understand what is involved in processing geospatial data in the rest of the book.
Deep learning is becoming increasingly important to solve a number of image processing tasks [1]. Among common algorithms, Convolutional Neural Network- and Recurrent Neural Network-based systems achieve state-of-the-art results on satellite and aerial imagery in many applications. For instance, synthetic aperture radar (SAR) interpretation with target recognition [2], classification of SAR time series [3], parameter inversion [4], hyperspectral image ...
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