Adaptive landmine detection and localization system based on incremental one-class classification

Khaoula Tbarki; Salma Ben Saïd; Riadh Ksantini; Zied Lachiri

doi:10.1117/1.JRS.12.036002

12 July 2018 Adaptive landmine detection and localization system based on incremental one-class classification

Khaoula Tbarki, Salma Ben Saïd, Riadh Ksantini, Zied Lachiri

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 3, 036002 (July 2018). https://doi.org/10.1117/1.JRS.12.036002

Abstract

Clearing large civilian areas of antipersonnel landmines is a very severe problem that should be solved efficiently. Although many methods have been developed for landmine detection and discrimination using ground penetrating radar data, the problem has not yet been properly solved, especially, as landmine and innocuous object classes are imbalanced. One-class classification is a competitive method for landmine detection as data are unbalanced, but it separates the target from outliers along the target class large variance directions, which results in higher error. As a solution, covariance-guided one-class support vector machine (COSVM) emphasizes low-variance projectional directions of the training data, which results in high accuracy and error minimization. However, in the case of a large-scale dataset, COSVM requires a large memory and enormous amount of training time. Moreover, it is inflexible with dynamic data. For these reasons, we investigate the effectiveness of incremental covariance-guided one-class support vector machine (ICOSVM) to build an adaptive landmine detection and localization system. In fact, the ICOSVM has the advantage of incrementally projecting the data onto low-variance directions, thereby improving detection performance. Experimental results have shown clearly the superiority and efficiency of the proposed landmine detection and localization system.

Citation Download Citation

Khaoula Tbarki, Salma Ben Saïd, Riadh Ksantini, and Zied Lachiri "Adaptive landmine detection and localization system based on incremental one-class classification," Journal of Applied Remote Sensing 12(3), 036002 (12 July 2018). https://doi.org/10.1117/1.JRS.12.036002

Received: 3 January 2018; Accepted: 22 June 2018; Published: 12 July 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available