Beyond texture: unveiling spiny crown-of-thorns starfish with multiresolution analysis

Abstract

Coral reefs are essential ecosystems in the vast expanses of oceans, nurturing various forms of marine life within their vibrant and expansive structures. However, these underwater paradises suffer considerable threat from the population explosions of crown-of-thorns starfish (COTS), which detrimentally affect scleractinian corals across the Indo-Pacific region. This study addresses the early drawback of solely relying on texture analysis for COTS detection, recognizing the associated insufficiency due to variability in reef substrates. By integrating multiresolution analysis employing wavelet transform, edge information, and texture analysis using gray-level co-occurrence probability, this approach employs crucial Haralick features refined for pattern recognition. This enables a more detailed understanding of COTS traits, including the detection of the numerous sharp spines that cover their upper bodies. This approach considerably enhances classification reliability, making notable progress with an impressive accuracy of 95.00% using the eXtreme Gradient Boosting (XGBoost) Classifier. Moreover, this model streamlines processing requirements by increasing computational and memory efficiencies, making it more resource-efficient than the current models. This advancement enhances detection and opens avenues for early intervention and future research. Furthermore, integrating the model with underwater imagery could enable citizen science initiatives and autonomous underwater vehicle (AUV) surveys. Empowering trained volunteers and equipping AUVs with this technology could considerably expand coral reef monitoring efforts. Early COTS outbreak detection allows for shorter response times, potentially mitigating the damage and facilitating targeted conservation strategies.