2017 conference paper

Semi-supervised deep generative models for change detection in very high resolution imagery

2017 ieee international geoscience and remote sensing symposium (igarss), 1063–1066.

By: C. Connors n & R. Vatsavai n 

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
Source: NC State University Libraries
Added: August 6, 2018

Increasing population, rapid urbanization, quest for biofuels, pollution, diseases, and adverse climate changes are some of the major drivers behind the changing surface of our planet. Timely monitoring and assessment of these changes, along with dissemination of accurate information, is important for policy makers, city planners, and humanitarian relief workers. Advances in remote sensing technologies have led to acquisition of very high resolution remote sensing imagery in the past decade. This data is highly useful for the aforementioned applications, and machine learning technology can be used to identify and quantify the changed regions. In this study we explore a semi-supervised deep generative model for change detection in very high resolution multispectral and bitemporal imagery. We constructed an auxiliary variational autoencoder that infers class labels without incurring high sample complexity costs. The resulting classifier was able to produce accurate predictions of real changes over images that appear significantly different due to environmental conditions (not real changes) while utilizing only a small set of labeled samples.