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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Ship Detection based on Rotation-invariant HOG Descriptors for Airborne Infrared Images
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Ship Detection based on Rotation-invariant HOG Descriptors for Airborne Infrared Images

机译：基于旋转不变HOG描述符的机载红外图像舰船检测

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摘要

Infrared thermal imagery is widely used in various kinds of aircraft because of its all-time application. Meanwhile, detecting ships from infrared images attract lots of research interests in recent years. In the case of downward-looking infrared imagery, in order to overcome the uncertainty of target imaging attitude due to the unknown position relationship between the aircraft and the target, we propose a new infrared ship detection method which integrates rotation invariant gradient direction histogram (Circle Histogram of Oriented Gradient. C-HOG) descriptors and the support vector machine (SVM) classifier. In details, the proposed method uses HOG descriptors to express the local feature of infrared images to adapt to changes in illumination and to overcome sea clutter effects. Different from traditional computation of HOG descriptor, we subdivide the image into annular spatial bins instead of rectangle sub-regions, and then Radial Gradient Transform (RGT) on the gradient is applied to achieve rotation invariant histogram information. Considering the engineering application of airborne and real-time requirements, we use SVM for training ship target and non-target background infrared sample images to discriminate real ships from false targets. Experimental results show that the proposed method has good performance in both the robustness and run-time for infrared ship target detection with different rotation angles.
机译：红外热成像由于其始终存在的应用而被广泛用于各种飞机。同时，近年来，从红外图像检测船只吸引了许多研究兴趣。在俯视红外图像的情况下，为了克服由于飞机与目标之间未知的位置关系而引起的目标成像姿态的不确定性，我们提出了一种新的红外舰船检测方法，该方法集成了旋转不变梯度方向直方图（圆定向梯度直方图（C-HOG）描述符和支持向量机（SVM）分类器。详细地，所提出的方法使用HOG描述符来表达红外图像的局部特征以适应照明的变化并克服海杂波效应。与传统的HOG描述符计算不同，我们将图像细分为环形空间区域，而不是矩形子区域，然后对梯度应用径向梯度变换（RGT）以实现旋转不变直方图信息。考虑到机载和实时需求的工程应用，我们使用SVM训练船舶目标和非目标背景红外样本图像，以区分真实船舶和虚假目标。实验结果表明，该方法在不同旋转角度下的红外舰船目标检测中均具有较好的鲁棒性和运行时间。

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