Work

IOPE-IPD: Water Properties Estimation Network Integrating Physical Model and Deep Learning for Hyperspectral Imagery

Year: 2025

Type: article

Abstract: Hyperspectral underwater target detection holds great potential for marine exploration and environmental monitoring. A key challenge lies in accurately estimating water inherent optical properties (IOPs) from hyperspectral imagery. To address these limitations, we propose a novel water IOP estimation network to support the interpretation of bathymetric models. We propose the IOPs physical model that focuses on the description of the water IOPs, describing how the concentrations of chlorophyll, colored dissolved organic matter, and detrital material influence the absorption and backscattering coefficients. Building on this foundation, we proposed an innovative IOP estimation network integrating a physical model and deep learning (IOPE-IPD). This approach enables precise and physically interpretable estimation of the IOPs. Specially, the IOPE-IPD network takes water spectra as input. The encoder extracts spectral features, while dual parallel decoders simultaneously estimate four key parameters. Based on these outputs, the absorption and backscattering coefficients of the water body are computed using the IOPs physical model. Subsequently, the bathymetric model is employed to reconstruct the water spectrum. Under the constraint of a consistency loss, the retrieved spectrum is encouraged to closely match the input spectrum. To ensure the IOPE-IPD’s applicability across various scenarios, multiple actual and Jerlov-simulated aquatic environments were used. Comprehensive experimental results demonstrate the robustness and effectiveness of our proposed IOPE-IPD over the compared method.

Source: Remote Sensing

DOI: 10.3390/rs17213546

Authors: Qi Li, Mingyu Gao, Ming Zhang, Junwen Wang, Jingjing Chen, Jinghua Li

Institution: Northwestern Polytechnical University

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDGs: Clean water and sanitation, Life below water

Open Access status: gold

Estimated APC paid: $3,258