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Layered compressive sensing reconstruction for non-scanning three-dimensional laser imaging

ABSTRACT As an active remote-sensing technique, three-dimensional (3D) laser radar enables accurate target detection and identification. However, the resolution of obtained image is limited by the integrated level of the sensor array. In this article, we propose a layered compressive sensing reconstruction method for high-resolution 3D laser imaging. The range profile is first reconstructed by applying a designed range observation matrix to the original sampling data. According to the distribution of range values in the reconstructed range profile, the original sampling data are divided into several layers and the corresponding intensity images are reconstructed by different algorithms. The global intensity image is generated by the reconstructed intensity images with different range compensation coefficients. In our simulations based on real data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2 (ASTER GDEM V2) and Landsat-8 Operational Land Imager (OLI), the proposed method realised 3D laser imaging for complex landforms with a pixel resolution of , and reached a satisfactory performance for reconstructions of the range profile (peak signal-to-noise ratio (PSNR) = 33.08 dB, feature similarity (FSIM) = 0.9450) and the intensity image (PSNR = 20.63 dB, FSIM = 0.8893) with a subrate of 0.25.