Underwater Sensing Overview

Principles

Definition

Underwater sensing is the academic field and technological discipline concerned with the detection, measurement, and interpretation of physical, chemical, biological, and acoustic parameters within aquatic environments using specialized sensor systems and methodologies. This interdisciplinary area investigates the characteristics and dynamics of underwater phenomena for applications ranging from environmental monitoring and resource management to navigation, communication, and scientific research.

Ontological type

Core Methods

Sensor Technologies

Applications

Key developments

Key Figures

Model-Based Shallow-Water Inversion era

J.G. Proakis[1] was a leading figure in this era, with affiliations at Tel Aviv University[3] and Northeastern University[4]. His key contribution was the phase-coherent digital communications for underwater acoustic channels[5], a work that demonstrated how preserving and exploiting phase information could significantly improve reliability and performance of underwater channels, foundational for later phase-sensitive inversion and coherent processing in model-based shallow-water sensing. Josko Catipovic[2] is another major contributor active during this era, with Northeastern University[4] among his institutional associations. Catipovic's work, as exemplified by the same phase-coherent communications study[5], helped establish phase-aware signal design and multipath exploitation that underpinned matched-field inversion and coherent imaging for seabed characterization and target localization in shallow-water environments.

Physics-Guided Data-Driven Vision era

Chongyi Li [1] is a prominent researcher whose work spans Army Medical University [3] and Nanyang Technological University [4] in this era. Her key contributions include the underwater scene prior inspired deep underwater image and video enhancement [7], Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior [8], and Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement [9], which advance physics-guided, data-driven restoration by providing robust priors and regularizers that improve perceptual color and contrast under scattering. Peixian Zhuang [2] is a leading figure at Xiamen University [5] and Tsinghua University [6], shaping underwater sensing practices in this era. Her work spans A retinex-based enhancing approach for single underwater image [10], Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network [11], and Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement [9], collectively advancing physics-guided data-driven restoration through Retinex-inspired illumination correction, learned priors via generative networks, and color-accurate contrast preservation.