High-Resolution Reconstruction and Image Classification Based on Optical Multi-Angle Information

Earth observation technology plays an important role in military reconnaissance, agriculture and forestry plant protection, emergency disaster relief, etc. In this paper, we mainly apply a prototype inversion algorithm: based on the multi-angle multi-spatial scale data of three scenes including trees, buildings and mixed objects simulated by the three-dimensional radiative transfer model LESS, we take the 500m/pixel coarse-resolution BRDF as an archetype priori information to reconstruct the high-resolution multi-angle information at 10m/pixel level. Then, sensitive feature indices are used to categorize the reconstructed 10m images, and the simulated 10m images are used as standard values to evaluate the classification accuracy. The results show that the average accuracies of BRDF inversion for the three scenes are 96.2%, 50.08% and 71.43%, respectively, and the plant class is more suitable for this inversion model. In terms of the classification accuracies, the three scenes are 60.52%, 96.84% and 76.60%, respectively, with building scene shows the highest accuracy.