A comparison on the effectiveness of different similarity measures for image classification

Authors:
Longteng Xu;Chih-Cheng Hung
Affiliations:
Southern Polytechnic State University, Marietta, GA;Southern Polytechnic State University, Marietta, GA
Venue:
Proceedings of the 49th Annual Southeast Regional Conference
Year:
2011

Citing 3
Cited 0

Remote Sensing: Digital Image Analysis

Remote Sensing: Digital Image Analysis
Remote Sensing, Third Edition: Models and Methods for Image Processing

Remote Sensing, Third Edition: Models and Methods for Image Processing
An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis

IEEE Transactions on Information Theory

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Abstract

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.