158 pages | 68 Color Illus.
In today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data.
Specific Features of this Book:
Introduction. Deep Learning Backgrounds. Patch Based Image Classification Using CNN. Patch Based Image Classification Using FCNN. Patch Based Image Classification with Hybrid Classifiers. Using Multiple Sources with Different Modalities. In Deep Learning Architectures. Semantic Segmentation of High-Resolution Images. Using a Convolutional Autoencoder Architecture to Perform Cloud Removal in Optical Image. Conclusions.