Statistical Perspective of SOM and CSOM for Hyper-Spectral Image Classification

Abstract

Classification of high-dimensional hyperspectral data is important in the remote sensing community. Although there are many supervised and unsupervised models for classification, a subset of them are consistent in reproducibility with high confidence intervals. This study shows the consistency and model accuracy of Self-Organizing map (SOM) and Cellular Self-Organizing Map (CSOM) over the benchmark pattern and hyperspectral datasets. A constructive analysis of both the models and optimal use cases are discussed in this work. t-SNE visualizations of these models were added to understand the models capability of capturing the topological density of the original data. In this study, CSOM is adopted for remote sensing image classification as an unsupervised clustering algorithm. The primary difference between SOM and CSOM is the updating principle of the neighboring neurons. Our experimental results show that CSOM performs better than SOM for the overlapping classes while SOM is efficient for classes which are well-separated or separable.