A lightweight hyperspectral image classification framework based on spectral domain discretization

In this paper, we propose a lightweight machine learning (ML) framework based on unsupervised spectral domain discretization for hyperspectral image (HSI) classification. Firstly, the high-dimensional HSI data is mapped into a discretized image by unsupervised learning method, and then the histogram statistics of discrete features are performed to align feature vectors. Finally, supervised ML method is used for classification, thus achieving a lightweight ML method of high-dimensional HSIs. Practical applications and comparative studies on three publicly available HSI datasets show that the framework approaches and surpasses deep learning models in classification accuracy while significantly compressing computational time consumption. The performance of six unsupervised clustering methods in HSI spectral domain discretization is compared in the study. Among them, K-means and GMM are superior in terms of classification accuracy. And SOM provides high classification accuracy while its discretization results are better interpretable due to better maintenance of topology during discretization.