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Infrared aerial small target detection based on digital image processing

机译：基于数字图像处理的红外空中小目标检测

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摘要

Aiming for the problem detection of infrared imaging aerial small target under complex background, an intelligent algorithm is presented based on digital image processing which mainly makes use of the theory of contourlet transform and BP(Back propagation) neural network. Firstly, this method transforms the infrared image from space domain to contourlet domain. Then, in order to suppress most complex background, this algorithm sets lowpass coefficients to zero because it includes most gentle background information of the infrared image. Furtherly, this method constructs a novel threshold formula for bandpass coefficients which is based on the classic formula and takes the directional energies into account for restraining the remained background edges and noises. Subsequently, the reverse transform is carried out and the preprocessing result is obtained. Secondly, taking pixel's grayscale, horizontal gradient, vertical gradient, diagonal gradient, neighborhood mean and neighborhood variance as input feature vector, a BP neural network which has three layers is constructed and trained so that the non-linear relationship between the features and the target or background's pixel. In the end, infrared small target is detected by this BP network which has finished the procedure of training. The experimental results show that the method given by this paper can not only realize the suppression for the infrared complex background effectively, but also detect the small target whose SNR(Signal Noise Ratio) value is above 2 steadily.
机译：针对复杂背景下红外成像空中小目标的问题检测，提出了一种基于数字图像处理的智能算法，该算法主要利用轮廓波变换和BP神经网络理论。首先，该方法将红外图像从空间域转换为轮廓波域。然后，为了抑制最复杂的背景，该算法将低通系数设置为零，因为它包含了红外图像的最柔和的背景信息。此外，该方法基于经典公式构造了用于带通系数的新阈值公式，并考虑了方向能量以抑制残留的背景边缘和噪声。随后，进行逆变换并获得预处理结果。其次，以像素的灰度，水平梯度，垂直梯度，对角梯度，邻域均值和邻域方差作为输入特征向量，构造并训练了三层的BP神经网络，使特征与目标之间具有非线性关系。或背景的像素。最后，通过该BP网络检测到红外小目标，并完成了训练程序。实验结果表明，该方法不仅可以有效地抑制红外复杂背景，而且可以稳定地检测出信噪比大于2的小目标。

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