  Goal oriented autonomous operation of space rovers has been known to increase
scientific output of a mission. In this work we present an algorithm, called
the RoI Prioritised Sampling (RPS), that prioritises Region-of-Interests (RoIs)
in an exploration scenario in order to utilise the limited resources of the
imaging instrument on the rover effectively. This prioritisation is based on an
estimator that evaluates the change in information content at consecutive
spatial scales of the RoIs without calculating the finer scale reconstruction.
The estimator, called the Refinement Indicator (RI), is motivated and derived.
Multi-scale acquisition approaches, based on classical and multilevel
compressed sensing, with respect to the single pixel camera architecture are
discussed. The performance of the algorithm is verified on remote sensing
images and compared with the state-of-the-art multi-resolution reconstruction
algorithms. At the considered sub-sampling rates the RPS is shown to better
utilise the system resources for reconstructing the RoIs.
