Dive into Aerial Remote Sensing Underwater Depth Estimation with Hyperspectral Imagery

Jiahao Qi, Xingyue Liu, Chen Chen, Dehui Zhu, Kangcheng Bin, Ping Zhong

Abstract: Visible spectrum images capture limited information from just three discrete bands, often resulting in suboptimal performance in underwater depth estimation (UDE) due to significant information loss from water absorption. In contrast, HSIs, which include hundreds of continuous bands, provide abundant spectral information that offers greater resilience against the adverse effects of water absorption. In this paper, we conduct a comprehensive study to investigate how spectral information can enhance remote sensing UDE through two key aspects: the benchmark dataset and the general framework. For the benchmark dataset, we construct a real-world hyperspectral UDE (HUDE) dataset ATR-HUDE, comprising approximately 500 synchronized hyperspectral and LiDAR data pairs collected from diverse coastal scenes and flight altitudes. Regarding the general framework, we integrate recent advances in state space models and physical imaging models to design a novel HUDE framework named HUDEMamba that estimates underwater depth using both model-driven and data-driven approaches. Experimental results on the constructed benchmark dataset validate the potential of HUDE and the effectiveness of HUDEMamba.