Kim Sungho, Song Woo-Jin, Kim So-Hyun
Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea.
Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea.
Sensors (Basel). 2016 Jul 19;16(7):1117. doi: 10.3390/s16071117.
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.
在嘈杂的杂乱环境中，使用合成孔径雷达（SAR）图像或红外（IR）图像很难检测远距离地面目标。基于SAR的探测器可以提供高检测率，但对背景散射噪声的误报率也很高。基于IR的方法可以检测热目标，但受天气条件影响很大。本文提出了一种新的目标检测方法，通过基于Adaboost的机器学习方案进行决策级SAR和IR融合，以实现高检测率和低误报率。该方法由个体检测、配准和融合架构组成。本文提出了一种使用改进的布尔地图视觉理论（modBMVT）和基于特征选择的融合的SAR和IR目标检测方法的单一框架。由于物理图像特征不同，以前的方法应用不同的算法来检测SAR和IR目标。一种针对IR目标检测优化的方法在SAR目标检测中产生了不成功的结果。本研究检查了图像特征，并通过在BMVT中插入中值局部平均滤波器（MLAF，预滤波器）和非对称形态学闭运算滤波器（AMCF，后滤波器），提出了一种统一的SAR和IR目标检测方法。原始的BMVT经过优化以检测小型红外目标。所提出的modBMVT可以通过MLAF去除热噪声和散射噪声，并在BMVT之后附加AMCF来检测扩展目标。在使用检测到的目标中心和区域进行蛮力对应搜索后，使用所提出的基于随机采样区域一致性（RANSARC）的单应性优化自动配准异构SAR和IR图像。最终目标通过使用Adaboost的基于特征选择的传感器融合来检测。通过在OKTAL-SE生成的合成数据库上基于特征选择的决策融合，所提出的方法显示出良好的SAR和IR目标检测性能。
