A Hybrid Random Forest and CNN Framework for Tile-Wise Oil-Water Classification in Hyperspectral Images

TL;DR

This paper introduces a hybrid framework combining Random Forest and CNN to improve oil-water classification in hyperspectral images by leveraging pixel-wise accuracy and spatial context, resulting in significant performance gains.

Contribution

The paper presents a novel hybrid approach that integrates Random Forest and CNN to enhance hyperspectral image classification by combining pixel accuracy with spatial context understanding.

Findings

Random Forest outperforms other models in pixel-wise classification.

The hybrid approach improves recall by 7.6%.

The method achieves a 0.84 F1 score and 0.99 AUC.

Abstract

A novel hybrid Random Forest and Convolutional Neural Network (CNN) framework is presented for oil-water classification in hyperspectral images (HSI). To address the challenge of preserving spatial context, the images were divided into smaller, non-overlapping tiles, which served as the basis for training, validation, and testing. Random Forest demonstrated strong performance in pixel-wise classification, outperforming models such as XGBoost, Attention-Based U-Net, and HybridSN. However, Random Forest loses spatial context, limiting its ability to fully exploit the spatial relationships in hyperspectral data. To improve performance, a CNN was trained on the probability maps generated by the Random Forest, leveraging the CNN's capacity to incorporate spatial context. The hybrid approach achieved 7.6% improvement in recall (to 0.85), 2.4% improvement in F1 score (to 0.84), and 0.54% improvement in AUC (to 0.99) compared to the baseline. These results highlight the effectiveness of combining probabilistic outputs with spatial feature learning for context-aware analysis of hyperspectral images.