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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images
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Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images

机译：基于高斯混合模型的舰船目标识别算法

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Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.
机译：由于遥感红外图像的分辨率较低，因此舰船目标的特征变得不稳定。如何识别具有模糊特征的船舶是一个悬而未决的问题。在本文中，我们提出了一种基于高斯混合模型（GMM）的新型舰船目标识别算法。在提出的算法中，主要有两个步骤。第一步，计算这些舰船目标图像的Hu矩，并根据舰船的矩特征对GMM进行训练。在第二步，将每个船舶图像的力矩特征分配给经过训练的GMM以进行识别。由于胡矩的大小，旋转，平移不变性以及GMM的功率特征空间描述能力，基于GMM的舰船目标识别算法可以可靠地识别舰船。大型模拟图像集的实验结果表明，我们的方法能够有效区分不同的船舶类型，并获得令人满意的船舶识别性能。

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