Journal

Multi-Class Pixel Certainty Active Learning Model for Land Cover Classification Using Hyperspectral Imagery

This paper introduces the Pixel Certainty Active Learning (PCAL) model for land cover classification using hyperspectral imagery (HSI). PCAL enhances classification accuracy by leveraging textural pattern information from Extended Differential Patterns (EDP). The approach integrates Distributed Intensity Filtering (DIF) for noise reduction and Histogram Equalization (HE) for image enhancement. Evaluated on the Pavia University and Indian Pines datasets, PCAL outperforms existing methods in classification accuracy and Kappa coefficient, demonstrating its effectiveness in handling pixel variations in remote sensing applications.