Graph-Based Interpolation for Remote Sensing Data

Remote sensing technologies, such as radiometry radar or synthetic-aperture radar (SAR) provide satellite ob- servations with different spatio-temporal resolutions. Some of these observations can be combined to enhance the resolution of satellite products. In this paper, we formulate the data interpolation problem as a signal reconstruction on a graph, where coarse observations are the signal on some of the nodes and high resolution data is used to select edge weights linking those nodes. Our method is initialized with a high resolution signal directly interpolated from coarse resolution observations obtained from a first instrument. Then, we construct a graph with edges determined by high resolution information obtained from geospatial data or another instrument. Finally, we solve a quadratic Laplacian optimization problem to ensure that the obtained high resolution observations are consistent with our original estimates while being smooth on the constructed graph. Preliminary results show that our approach leads to better remote sensed data reconstruction when compared against traditional spatial filtering and interpolation techniques.