In this project, our focus is on the design, testing, and evaluation of compressive sensing (CS) architectures for enhancing the high-quality video acquisition and delivery capabilities of remote sensing devices that will enable them to provide efficient remote imaging in aerial and terrestrial surveillance. We will address limitations to current video coding methods which restrict the use of remote sensing devices to offering only a low-quality streaming video to the user. In order to overcome these limitations, novel algorithms which go well beyond the currently developed techniques and standards are needed. Equally importantly, the proposed methods must be developed with the hardware constraints of the platform of operation in mind, including restrictions regarding computational, memory, and power consumption, as well as the available reserved bandwidth. The proposed activities are designed in order to address these challenges. An important aspect of this project is the conscious exploitation of the inherent encryption of the signal that the compressive sensing methodology offers. We intend to use this property to enrich the signal intelligence capabilities of surveillance systems. The proposed consortium consists of partners with complementary expertise which covers all the involved research areas, from the point of capturing the video imaging content to the point that it is dynamically reconstructed and analyzed through CS reconstruction techniques by the user.