An Improved Ant Colony Algorithm for Optimized Band Selection of Hyperspectral Remotely Sensed Imagery

Xiaohui Ding,Shuqing Zhang,Chunlei Jiang,Huapeng Li,Patricia Dale,Ji Yang,Xiangcong Chen

doi:10.1109/access.2020.2971327

Abstract

The ant colony algorithm (ACA) has been widely used for reducing the dimensionality of hyperspectral remote sensing imagery. However, the ACA suffers from problems of slow convergence and of local optima (caused by loss of population diversity). This paper proposes an improved ant colony algorithm (IMACA) based band selection algorithm (IMACA-BS), to overcome the two shortcomings of the standard ACA. For the former problem, a pre-filter is applied to improve the heuristic desirability of the ant colony system; the Pearson’s similarity measurement of the degree of redundancy among the selected bands is taken as one of the terms in the heuristic function, and this further accelerates the convergence of the IMACA-BS. For the latter problem, a pseudo-random rule and an adaptive information update strategy are, respectively, introduced to increase the population diversity of the ant colony system. The effectiveness of the proposed algorithm was evaluated on three public datasets (Indian Pines, Pavia University and Botswana datasets), and compared with a series of benchmarks. Experimental results demonstrated that the IMACA-BS consistently achieved the highest overall classification accuracies and significantly outperformed other benchmarks over all of the three experiments. The proposed IMACA-BS is, therefore, recommended as an effective alternative for band selection of hyperspectral imagery.

Highlights

Hyperspectral image (HSI) containing hundreds of spectral bands provides abundant spectral information about on-ground objects
Each algorithm was repeatedly run 10 times on each dataset, and the average overall classification accuracy (OA) of the band subsets were calculated for different numbers of bands (5, 10, 15, 20, 25, 30, 35, and 40)
The parameters of the aca-based band selection algorithm were set according to previous experience [30], [31]

Summary

Introduction

Hyperspectral image (HSI) containing hundreds of spectral bands provides abundant spectral information about on-ground objects. Leading to the curse of dimensionality phenomena [2]. It is, essential to reduce data dimensions to facilitate the analysis of hyperspectral data. Feature extraction and feature selection are two kinds of typical data reduction techniques [3]. Feature extraction techniques (e.g., principal components analysis), are developed to compress data by using complex mathematical transformations. Though these methods may extract useful features from the HSI data sets, the interpretable primitive physical significance of the data is often lost in the data transformation process

Methods

Results

Discussion

Conclusion