Understanding Non-optical Remote-sensed Images: Needs, Challenges and Ways Forward
It highlights a domain-specific problem for users in fields like disaster response and agriculture, but is incremental as it reviews existing approaches without introducing new methods.
The paper addresses the growing need for real-time understanding of non-optical remote-sensed images, such as radar and hyperspectral data, for applications like disaster management and precision agriculture, but does not present specific results or numbers.
Non-optical remote-sensed images are going to be used more often in man- aging disaster, crime and precision agriculture. With more small satellites and unmanned air vehicles planning to carry radar and hyperspectral image sensors there is going to be an abundance of such data in the recent future. Understanding these data in real-time will be crucial in attaining some of the important sustain- able development goals. Processing non-optical images is, in many ways, different from that of optical images. Most of the recent advances in the domain of image understanding has been using optical images. In this article we shall explain the needs for image understanding in non-optical domain and the typical challenges. Then we shall describe the existing approaches and how we can move from there to the desired goal of a reliable real-time image understanding system.