Dominant Sets Based Band Selection in Hyperspectral Imagery

TL;DR

This paper introduces a dominant sets based band selection framework for hyperspectral imagery that reduces data size and improves classification accuracy by selecting the most representative spectral bands, with low computational complexity.

Contribution

It proposes a novel dominant sets clustering method for spectral band selection tailored for hyperspectral data, outperforming existing methods in accuracy.

Findings

Better classification accuracy on Pavia and Salinas datasets

Lower computational complexity compared to state-of-the-art methods

Effective reduction of spectral bands for specific applications

Abstract

Hyperspectral imagery is composed of huge amount of data which creates significant transmission latencies for communication systems. It is vital to decrease the huge data size before transmitting the Hyperspectral imagery. Besides, large data size leads to processing problems, especially in practical applications. Moreover, due to the lack of sufficient training samples, Hughes phenomena occur with huge amount of data. Feature selection can be used in order to get rid of huge data problems. In this paper, a band selection framework is introduced to reduce the data size and to find out the most proper spectral bands for a specific application. The method is based on finding "dominant sets" in hyperspectral data, so that spectral bands are clustered. From each cluster, the band that reflects the cluster behavior the most is selected to form the most valuable band set in the spectra for a specific application. The proposed feature selection method has low computational complexity since it performs on a small size of data when realizing the feature selection. The aim of the study is to find out a general framework that can define required bands for classification without requiring to perform on the whole data set. Results on Pavia and Salinas datasets show that the proposed framework performs better than the state-of-the-art feature selection methods in terms of classification accuracy.