A Geometry-Aware Consistent Constraint for Height Estimation From a Single SAR Imagery in Mountain Areas

Height estimation from a single synthetic aperture radar (SAR) imagery has shown a great potential in real-time scene understanding and environment detection. It is a mathematical ill-posed problem for that a single 2-D image may be projected from multiple 3-D scenes. Then, the problem is that the accuracy of height estimation from a single SAR image is not high enough without prior knowledge, especially in mountain areas. Thus, we propose a geometry-aware consistent constraint (GACC) to improve the performance of single SAR height estimation. The simulated SAR images generated from height maps are used to establish high-precision transformation between map and radar coordinates in mountain areas. The main idea of GACC is that the simulated SAR image generated from estimated height map should be consistent with the ground-truth simulated SAR image. A sparse height (SH) map is included as the supplementary inputs to further improve the accuracy of estimated height maps. Comparison experiments are performed in Geermu, Huangshan, and Guiyang datasets, and the results show that the root-mean-square error (RMSE) of the estimated height map by U-shaped convolution neural network (Unet) with GACC and 0.0434% SH gets reduced by about 98%, 78%, and 94% compared with that by Unet in the three datasets.

Automated summary

Height estimation from a single synthetic aperture radar (SAR) imagery has great potential but suffers from low accuracy in mountain areas. To address this issue, a geometry-aware consistent constraint (GACC) method is proposed. It uses simulated SAR images and a sparse height map (SH) to improve the performance of single SAR height estimation. Experimental results demonstrate a significant reduction in root-mean-square error (RMSE) compared to traditional methods.