Fusion of multi-scale DEMs from Chang’E-3 descent and Navcam images using compressed sensing method

• Published in: Planetary Remote Sensing and Mapping pp. 241 - 250
• Main Authors: Peng, M., Wan, W., Liu, Z., Di, K.
• Format: Book Chapter
• Language: English
• Published: CRC Press 2019
• Edition: 1
• Subjects: Shuttle Radar Topography Mission DEM; Seamless DEM; Source DEMs; Reference DEM; Kriging Interpolation; Real DEMs; Compressed Sensing; OMP Algorithm; Sparse Coefficient Vector; Dictionary Training; High Resolution DEM; High Resolution DEM Data; Single Scattering Albedo; Underdetermined Linear Systems; Minimum Patch Size; RFM; DEM Data; Patch Borders; Sparse Coefficients; DEM Dataset; DOM; Low Resolution DEMs; High Quality DEM; Training Patches; Sparse Representation
• ISBN: 9781138584150; 1138584150
• DOI: 10.1201/9780429505997-16

The multi-source digital elevation models (DEMs) generated using images acquired during Chang’e-3’s descent and landing phases and after landing contain supplementary information that allows a higher-quality DEM to be produced by fusing multi-scale DEMs. The proposed fusion method consists of three steps. First, source DEMs are split into small DEM patches, which are classified into a few groups by local density peak clustering. Next, the grouped DEM patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined to form a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit algorithm is used to achieve sparse representation. We use real DEMs generated from Chang’e-3 descent images and navigation camera stereo images to validate the proposed method. Through our experiments, we reconstruct a seamless DEM with the highest resolution and the broadest spatial coverage of all of the input data. The experimental results demonstrate the feasibility of the proposed method.