a comparison on the effectiveness of different similarity measures for image classification
In this study, we explore the effectiveness of some frequently used data clustering algorithms for multispectral image classification. These algorithms are the K-means algorithm using Euclidean distance (ED), Spectral Angle Mapper (SAM), and Spectral Information Measure (SIM). The objective is to compare the efficiency of different similarity measures used for image classification algorithms. Preliminary experimental results show that the SIM measure is better than those of ED and SAM measures for the hyperspectral Indian Pine images.
euclidean distance spectral angle mapper (sam) spectral information measure (sim)