  The performance of Automated Recognition (ATR) algorithms on side-scan sonar
imagery has shown to degrade rapidly when deployed on non benign environments.
Complex seafloors and acoustic artefacts constitute distractors in the form of
strong textural patterns, creating false detections or preventing detections of
true objects. This paper presents two online seafloor characterisation
techniques to improve explainability during Autonomous Underwater Vehicles
(AUVs) missions. Importantly and as opposed to previous work in the domain,
these techniques are not based on a model and require limited input from human
operators, making it suitable for real-time onboard processing. Both techniques
rely on an unsupervised machine learning approach to extract terrain features
which relate to the human understanding of terrain complexity. The first
technnique provides a quantitative, application-driven terrain characterisation
metric based on the performance of an ATR algorithm. The second method provides
a way to incorporate subject matter expertise and enables contextualisation and
explainability in support for scenario-dependent subjective terrain
characterisation. The terrain complexity matches the expectation of seasoned
users making this tool desirable and trustworthy in comparison to traditional
unsupervised approaches. We finally detail an application of these techniques
to repair a Mine Countermeasures (MCM) mission carried with SeeByte autonomy
framework Neptune.
