Machine Learning-Constrained Semi-Analysis Model for Efficient Bathymetric Mapping in Data-Scarce Coastal Waters

Exploring foci of: Remote Sensing • Vol 17 • No 18 Machine Learning-Constrained Semi-Analysis Model for Efficient Bathymetric Mapping in Data-Scarce Coastal Waters September 2025 • Qifei Wang, Xianliang Zhang, Zhongqiang Wu, Chang Han, Longwei Zhang, Peng Xu, Zhihua Mao, Yueming Wang, Changxing Zhang Nearshore bathymetry is critical for coastal management and ecology. While airborne hyperspectral remote sensing provides high-resolution image data, obtaining rapid and accurate bathymetric inversion in coastal areas lacking in situ data remains challenging. The widely used Hyperspectral Optimization Process Exemplar (HOPE) achieves high accuracy but suffers from computational inefficiency, making it impractical for large-scale, high-resolution datasets. By contrast, HOPE-Pure Water (HOPE-PW) offers computational… Open Article Page

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