Impact of Remote Sensing Domain Knowledge on Satellite Imagery Classification of the Amazon Rainforest

Abstract

Satellite imagery with finer spatial resolution provides an opportunity to detect small- scale changes in forest cover. With recent improvements of techniques in the field of computer vision, convolutional neural networks can be trained with satellite imagery through a combination of deep learning and remote sensing principles. The aim of this thesis is to identify land cover types, land use types, and atmospheric conditions present in satellite imagery of the Amazon rainforest using deep learning models. Implementing, training, and testing models provide methods for detecting and identifying mechanisms of deforestation and shape understanding needed to protect the world’s forests. Our trained models demonstrate the ability to identify features in satellite imagery of the Amazon rainforest to a high degree of accuracy. Additionally, we establish their capability to detect small-scale forest cover changes over time.