用随机森林与卷积网络结合，提升高光谱图像油水分类精度。
A Hybrid Random Forest and CNN Framework for Tile-Wise Oil-Water Classification in Hyperspectral Images
- 先用随机森林做像素分类，再用卷积网络学习概率图的空间关系。
- 召回率提升7.6%至0.85，F1得分达0.84，AUC达0.99。
- 适合需要空间上下文感知的遥感图像分类任务。
本文提出一种混合随机森林与卷积神经网络（CNN）的框架，用于高光谱图像（HSI）中的油水分类。为保留空间上下文，图像被分割为不重叠的小块（tile）作为训练、验证和测试基础。随机森林在像素级分类中表现优异，优于XGBoost、基于注意力的U-Net及HybridSN等模型。然而其缺乏空间上下文信息，限制了对高光谱数据空间关系的利用。为此，将随机森林生成的概率图输入CNN，利用其空间特征建模能力进行优化。该混合方法相较基线模型，召回率提升7.6%（达0.85），F1分数提高2.4%（达0.84），AUC提升0.54%（达0.99），验证了融合概率输出与空间特征学习在上下文感知分析中的有效性。
原文摘要 · Abstract (English)
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.
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