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Sensors 2016, 16(7), 1117; doi:10.3390/s16071117

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

1
Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea
2
Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea
3
Agency for Defense Development, 111 Sunam-dong, Daejeon 34186, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Changzhi Li
Received: 9 May 2016 / Revised: 22 June 2016 / Accepted: 13 July 2016 / Published: 19 July 2016
(This article belongs to the Special Issue Non-Contact Sensing)

Abstract

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE. View Full-Text
Keywords: synthetic aperture radar; infrared; target detection; sensor fusion; machine learning; feature selection; OKTAL-SE synthetic aperture radar; infrared; target detection; sensor fusion; machine learning; feature selection; OKTAL-SE
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kim, S.; Song, W.-J.; Kim, S.-H. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection. Sensors 2016, 16, 1117.

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