A Model Distillation Approach for Explaining Black-Box Models for Hyperspectral Image Classification

Recent studies in remote sensing reveal that complex nonlinear learning models such as deep learning or ensemble-based learning are mostly preferred compared to shallow machine learning methods in solving many problems such as classification, image fusion, change detection, unmixing, and object recognition. The fact that much remote sensing data can be obtained quickly, abundantly, and free of charge, and the increasing computing power of computers with developing technology, are why such methods are preferred. With the emergence of big data, these methods provide more effective solutions than in past years, and they can outperform shallow machine learning methods in many remote sensing applications. Despite their high accuracy, such learning models have several limitations due to their black-box structure. Because of the high nonlinearity in predictive models, these models cannot explain why and how decisions are made. This paper presents a global model distillation approach to replace a black-box model with a fully explainable surrogate model utilizing polynomial chaos expansion. Preliminary results show that the proposed method can accurately replace a complex nonlinear model with a simpler one in hyperspectral image classification.