Title: Probabilistic rough set-based band selection method for hyperspectral data classification

Authors: Min Li; Shaobo Deng; Lei Wang; Jun Ye

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China

Abstract: This paper proposes an innovative band selection algorithm called probabilistic rough set-based band selection (PRSBS) algorithm. The proposed PRSBS is a supervised band selection algorithm with efficiency for it only needs to calculate the first-order significance measure. The main novelty of the proposed PRSBS algorithm lies in the criterion function which measures the effectiveness of considered band. The PRSBS algorithm uses a probabilistic distribution dependency as the relevance measure between the bands and class labels, which can effectively measure the uncertainty in both the positive and the boundary samples in a dataset. We compared the proposed PRSBS with the most relevant band selection algorithm RSBS on three different hyperspectral datasets, the experimental results show that the PRSBS has better results than the RSBS. Moreover, the PRSBS algorithm runs significantly faster than the RSBS algorithm, which makes it a proper choice for band selection in hyperspectral image dataset.

Keywords: band selection; probabilistic rough set; hyperspectral image; classification.

DOI: 10.1504/IJCSE.2020.105211

International Journal of Computational Science and Engineering, 2020 Vol.21 No.1, pp.38 - 48

Received: 26 Jun 2017
Accepted: 18 Nov 2017

Published online: 11 Feb 2020 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article