Filling SRTM Void Data Via Conditional Adversarial Networks

We develop conditional adversarial networks (CAN) framework for filling Shuttle Radar Topography Mission (SRTM) void data. We train a CAN model in terms of using incomplete and complete terrain images as inputs and outputs, respectively. In this scenario, the CAN model characterizes a void-to-filling translation and thus learns the knowledge for void data restoration. Furthermore, in order to make the void-filled images more realistic and less blurry, we employ a L1 norm to constrain the CAN training process. The trained CAN is used for restoring the incomplete SRTM data. Experimental comparisons reveal that our framework outperforms the interpolation strategy. Additionally, experiments also validate that our method performs well in restoring large areas of SRTM missing data, such as rectangular areas from 36 ^° N 74 ^° E to 37 ^° N 75 ^° E .