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Related Experiment Video

Updated: Oct 8, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Published on: June 18, 2021

Water column compensation workflow for hyperspectral imaging data.

Deep Inamdar¹, Gillian S L Rowan¹, Margaret Kalacska¹

¹Applied Remote Sensing Laboratory, Department of Geography, McGill University, Montréal, QC H3A 0B9, Canada.

|January 5, 2022

Summary

This summary is machine-generated.

This study presents a MATLAB workflow to process hyperspectral imaging data, compensating for water column effects in clear to moderately clear waters. The method simplifies data for accurate shallow water bottom type analysis.

Keywords:

Depth invariant index (DII)Hyperspectral imaging Principal component analysis (PCA)Water column compensation

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Area of Science:

Remote Sensing
Ocean Optics
Geospatial Analysis

Background:

Hyperspectral imaging is valuable for aquatic environment monitoring.
Water column properties complicate shallow water bottom type analysis.
Existing methods require efficient data processing for hyperspectral datasets.

Purpose of the Study:

To develop and present a data processing workflow for hyperspectral imaging data.
To compensate for water column effects in shallow, clear to moderate optical water types.
To provide a user-friendly MATLAB script for implementing the workflow.

Main Methods:

Implemented Lyzenga's (1978, 1981) method to generate depth invariant indices from spectral band pairs.
Applied correlation-based feature selection to reduce redundant indices from high-dimensional hyperspectral data.
Utilized principal component transformation for dimensionality reduction and feature extraction.

Main Results:

Generated depth invariant indices and a principal component image from hyperspectral data.
Successfully compensated for water column effects in shallow aquatic environments.
Demonstrated the workflow's capability to extract bottom type information.

Conclusions:

The developed workflow effectively processes hyperspectral data for water column compensation.
The principal component transformation efficiently reduces dimensionality while retaining key features.
The output facilitates detailed analysis of bottom types in shallow, clear to moderate optical waters.