Combined No–Reference Metrics for Quality Assessment of Reconstructed Satellite Lidar Data

In this paper, experimental results concerning the application of no-reference image quality metrics for automatic quality assessment of reconstructed satellite light detection and ranging (LiDAR) data are discussed. Since satellite LiDARs provide much sparser data compared to airborne systems and their sending to Earth requires additional compression, the received data should be reconstructed. For this purpose, generative neural networks can be applied, including generative diffusion models; however, they require the choice of appropriate parameters, and the reconstruction results should be reliably assessed. Although the physical representation of data is related to the number of received photons, considering the novel data representation based on hyper-height data cubes, it is possible to analyze individual percentiles, representing canopy height models or digital terrain models, as grayscale images. Therefore, their quality may be evaluated using image quality assessment metrics. Due to the lack of reference data, in the application considered, only no-reference metrics may be efficiently applied, even though they have lower correlation with subjective quality perception compared to full-reference metrics. Therefore, the idea of designing combined no-reference metrics optimized for the highest correlation with subjective quality scores is discussed, along with the presentation of the obtained correlation results.