225 pages | 8 Color Illus. | 30 B/W Illus.
This book provides key insights into the world of Deep Learning pertaining to satellite image understanding. It highlights what differentiates satellite image datasets from other natural or synthetic images and how to tackle problems specific to these imagery data. From answering questions like how to select optimal training data to weekly supervised and unsupervised learning and how to tackle loosely labeled data, it is a valuable source of information for anyone interested in understanding the theory behind satellite image analytics and provides key insights on the application of various state-of-the-art Deep Learning algorithms on these datasets.
Unsupervised learning for satellite imagery. Working with loosely labeled data. Classification vs Segmentation. Semi-supervised learning. Using adversarial learning. Post-processing – Structured Prediction. Using hyperspectral images. Combining hand-crafted features and deep learning. Active Learning. Transfer Learning. Choosing optimal training data. Understanding relation between training data and optimal model size . Effect of data augmentation. Effects of skip connections for creating bigger networks. How satellite image processing differs from natural image processing.