# Explainable ML for Internal Wave Detection in SAR Imagery

A research project on detecting oceanic internal waves in synthetic aperture radar (SAR) imagery with explainable machine learning. I am one of the paper's authors, alongside my colleague and our faculty supervisor, and contributed the literature review and methodology discussions.

- Role: Research Contributor (Literature Review & Methodology Discussion) (team project)
- Date: Jan 2026 – Present
- Status: Paper under journal review
- Categories: research
- Tech: Python, Machine Learning, SAR Image Processing, Feature Engineering, XGBoost, SHAP

## Overview

Internal waves are large-amplitude waves that travel within the ocean rather than at its surface. They leave faint signatures on the sea surface that synthetic aperture radar (SAR) can capture, so satellite imagery is a practical source for detecting them. This research project studies how machine learning can detect internal-wave regions in SAR images, and how explainability methods can show which image characteristics drive those detections.

## Project Scope

The project compares detection approaches that draw on frequency-domain analysis, textural and morphological descriptors, and machine-learning models such as gradient-boosted trees, with SHAP used to explain model behaviour in terms of spectral, textural and morphological features.

## My Contribution

This was a collaborative project carried out under the supervision of a faculty member. The implementation and main experimental work were led by a colleague, and I am a co-author of the paper together with them and our supervisor. My part was:

- **Literature review:** I surveyed the research on SAR-based internal-wave detection, covering frequency-domain, textural, morphological and machine-learning methods, and summarised it for the team.
- **Methodology discussion:** I took part in discussions comparing alternative feature-engineering and machine-learning approaches, and in deciding the direction of the experiments.
- **Interpretation:** I joined the discussions on reading the results and the explainability findings.

## Status

The paper, co-authored with my colleague and our faculty supervisor, is currently under journal review.

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