A Systematic Evaluation of CNN Configurations for Multiclass Oil Spill Classification in Hyperspectral Images

Oil spills represent a severe threat to aquatic ecosystems, requiring rapid and reliable detection methods to support environmental response. Hyperspectral imaging (HSI) offers high spectral resolution for distinguishing hydrocarbon types, but its effective use depends on the performance and robustness of deep learning (DL) models, especially under data-limited conditions. This study presents a systematic evaluation of convolutional neural network (CNN) configurations for oil spill classification in visible-near-infrared (VNIR) hyperspectral data, examining the influence of architectural depth and hyperparameters such as the number of convolutional kernels, neuron density, and dropout rate. Two architectures were tested across 54 configurations and two training set sizes (259 and 518 samples). Results show that a compact architecture with an additional max pooling layer achieved near-perfect accuracy (>0.99) with reduced complexity and greater robustness, outperforming its deeper counterpart. Importantly, this study reveals that under small-sample scenarios, optimal performance can still be achieved by carefully balancing model capacity, favouring moderate convolutional depth and high neuron density, while avoiding over-regularisation. These findings provide practical guidance for designing efficient CNNs for UAV-based oil spill monitoring and lay the groundwork for future integration into local real-time processing pipelines and transfer learning applications.