Earth Observation Big Data Intelligence: theory and practice of deep learning and big data mining

Abstract

In the big data era of earth observation, deep learning and other data mining technologies become critical to successful end applications. Over the past several years, there has been exponentially increasing interests related to deep learning techniques applied to remote sensing including not only hyperspectral imagery but also synthetic aperture radar (SAR) imagery. This tutorial has two parts. The first half introduces the basic principles of machine learning, and the evolution to deep learning paradigms. It presents the methods of stochastic variational and Bayesian inference, focusing on the methods and algorithms of deep learning generative adversarial networks. Since the data sets are organic part of the learning process, the EO dataset biases pose new challenges. The tutorial answers to open questions on relative data bias, cross-dataset generalization, for very specific EO cases as multispectral, SAR observation with a large variability of imaging parameters and semantic content. The second half introduces the theory of deep neural networks and the practices of deep learning-based remote sensing applications. It introduces the major types of deep neural networks, the backpropagation algorithms, programming toolboxes, and several examples of deep learning-based remote sensing imagery processing.