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首页> 外文会议>Electro-Optical and Infrared Systems: Technology and Applications IV; Proceedings of SPIE-The International Society for Optical Engineering; vol.6737 >Naval target classification by fusion of IR and EO sensors
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Naval target classification by fusion of IR and EO sensors

机译：红外和EO传感器融合对海军目标进行分类

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摘要

This paper describes the classification function of naval targets performed by an infrared camera (IR) and an electro-optical camera (EO) that operate in a more complex multisensor system for the surveillance of a coastal region. The following naval targets are considered: high speed dinghy, motor boat, fishing boat, oil tanker. Target classification is automatically performed by exploiting the knowledge of the sensor confusion matrix (CM). The CM is analytically computed as a function of the sensor noise features, the sensor resolution, and the dimension of the involved image database. For both the sensors, a database of images is generated exploiting a three-dimensional (3D) Computer Aided Design (CAD) of the target, for the four types of ship mentioned above. For the EO camera, the image generation is simply obtained by the projection of the 3D CAD on the camera focal plane. For the IR images simulation, firstly the surface temperatures are computed using an Open-source Software for Modelling and Simulation of Infrared Signatures (OSMOSIS) that efficiently integrates the dependence of the emissivity upon the surface temperature, the wavelength, and the elevation angle. The software is applicable to realistic ship geometries. Secondly, these temperatures and the environment features are used to predict realistic IR images. The local decisions on the class are made using the elements of the confusion matrix of each sensor and they are fused according to a maximum likelihood (ML) rule. The global performance of the classification process is measured in terms of the global confusion matrix of the integrated system. This analytical approach can effectively reduce the computational load of a Monte Carlo simulation, when the sensors described here are introduced in a more complex multisensor system for the maritime surveillance.
机译：本文介绍了由红外摄像机（IR）和光电摄像机（EO）执行的海军目标分类功能，这些摄像机在更复杂的多传感器系统中运行，以监视沿海地区。考虑以下海军目标：高速小艇，摩托艇，渔船，油轮。目标分类是通过利用传感器混淆矩阵（CM）的知识自动执行的。根据传感器噪声特征，传感器分辨率和所涉及图像数据库的尺寸来分析计算CM。对于这两种传感器，针对上述四种类型的舰船，利用目标的三维（3D）计算机辅助设计（CAD）生成图像数据库。对于EO相机，只需将3D CAD投影在相机焦平面上即可获得图像生成。对于红外图像仿真，首先使用用于红外特征码建模和仿真的开源软件（OSMOSIS）计算表面温度，该软件有效地集成了发射率对表面温度，波长和仰角的依赖性。该软件适用于实际的船舶几何形状。其次，这些温度和环境特征用于预测真实的红外图像。使用每个传感器的混淆矩阵的元素对类别进行局部决策，并根据最大似然（ML）规则对它们进行融合。分类过程的整体性能是根据集成系统的整体混淆矩阵来衡量的。当将此处介绍的传感器引入更为复杂的海上监视多传感器系统中时，这种分析方法可以有效地减少Monte Carlo模拟的计算量。

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