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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Gaussian Scale-Space Enhanced Local Contrast Measure for Small Infrared Target Detection
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Gaussian Scale-Space Enhanced Local Contrast Measure for Small Infrared Target Detection

机译：高斯尺度空间增强了小红外目标检测的本地对比度测量

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摘要

Robust small-target detection plays an important role in the infrared (IR) search and track system, but it is still a challenge to detect small IR target under complex background. In this letter, an effective method inspired by the scale-space theory and the contrast mechanism of the human vision system is proposed. First, Gaussian scale-space (GSS) of an IR image is constructed by the convolution of a variable-scale Gaussian function. Second, the gray features of the local image can be directly represented by downsampling in a scale image, and enhanced local contrast measure (ELCM) is defined to enhance small target and suppress complex background. Then, the saliency map is obtained by using max-pooling operation, and an adaptive threshold is adapted to segment real targets. Experimental results on a test set with three real IR sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression, and shows strong robustness under complex background. Especially, the proposed method has high computational efficiency, which can improve detection speed.
机译：强大的小目标检测在红外（IR）搜索和轨道系统中起着重要作用，但在复杂背景下检测小红外目标仍然是一个挑战。在这封信中，提出了一种由尺度空间理论的有效方法和人类视觉系统的对比机制。首先，IR图像的高斯刻度空间（GSS）由变量级高斯函数的卷积构成。其次，局部图像的灰色特征可以通过在比例图像中下采样直接表示，并且被定义增强的本地对比度（ELCM）以增强小目标并抑制复杂背景。然后，通过使用MAX池操作获得显着性图，自适应阈值适于分段真实目标。具有三个真正红外序列的试验组的实验结果表明，该方法在目标增强和背景抑制方面具有良好的性能，并在复杂背景下显示出强大的鲁棒性。特别是，该方法具有高计算效率，可以提高检测速度。

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