Targeted change detection in remote sensing images
This work addresses the need for precise change detection in remote sensing for applications like environmental monitoring or urban planning, but it appears incremental as it builds on existing deep learning methods without claiming major breakthroughs.
The paper tackles the problem of detecting specific changes in time-series satellite images by proposing a formal problem statement and a new deep learning framework for targeted change detection, demonstrating its applicability in business cases.
Recent developments in the remote sensing systems and image processing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images (so called targeted change detection). In this paper we propose a formal problem statement that allows to use effectively the deep learning approach to analyze time-dependent series of remote sensing images. We also introduce a new framework for the development of deep learning models for targeted change detection and demonstrate some cases of business applications it can be used for.