Abstract—In this paper we propose an operational superresolution
(SR) approach for multi-temporal and multi-angle remote
sensing imagery. The method consists of two stages: registration
and reconstruction. In the registration stage a hybrid patch-based
registration scheme that can account for local geometric distortion
and photometric disparity is proposed. Obstacles like clouds
or cloud shadows are detected as part of the registration process.
For the reconstruction stage a SR reconstruction model composed
of the L1 norm data fidelity and total variation (TV) regularization
is defined, with its reconstruction object function being efficiently
solved by the steepest descent method. Other SR methods can be
easily incorporated in the proposed framework as well. The proposed
algorithms are tested with multi-temporal and multi-angle
WorldView-2 imagery. Experimental results demonstrate the effectiveness
of the proposed approach.