PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data

Forest height inversion based on polarimetric interferometric synthetic aperture radar (PolInSAR) data has demonstrated significant potential for producing large-scale, high-resolution forest height maps, and has been the subject of extensive research in recent decades. Machine learning and deep learning (DL) techniques frequently utilize the height of the forest derived from LiDAR as labels, exhibiting superior inversion accuracy. Nevertheless, owing to the complex terrain of forest areas, it is difficult to ensure the inversion accuracy when training in a specific forested scene and then applying it to other areas. Meanwhile, the lack of training samples imposes additional constraints on the efficacy of supervised DL-based methods. Given this context, an unsupervised framework for forest height inversion that can be used with several existing DL models and does not require previous knowledge from LiDAR data is very appealing. In this paper, we delve into this framework by introducing a novel terrain compensation strategy which allows the framework to be adapted to different forest terrain scenarios. Furthermore, a more efficient network architecture called ResUnet++ is used to extensively extract forest height information from pseudo-labels, resulting in the novel adaptive Polarimetric Interferometric Deep Learning-based Framework (PIDLF+). Three study sites with different terrain characteristics, i.e., Lope, Pongara and Rabi, were selected for ´ validation. The experiments show that the proposed PIDLF+ works well in different forest terrains and improves accuracy. It achieved 8.69 m RMSE and 0.94 R2 in the Lope site, 11.31 m ´ RMSE and 0.90 R2 in the Pongara site, and 8.86 m RMSE and 0.92 R2 in the Rabi site. The code will be available at https://github.com/luhailaing-max/PIDLF-Plus.