小波神经网络自学习算法用于红外图像分割

在红外动目标序列图像跟踪过程中,由于目标本身的红外特征具有较大的不可预测性,使ATR系统在目标探测阶段产生大量的虚警讯息。因此,必须设法在复杂背景抑制段将虚警探测讯息滤除掉。提出了一种新颖的基于小波神经网络构架的FLIR图像分割技术,旨在将小波变换的时-频局域特性和神经网络的自学习能力相结合,从而使FLIR图像的分割算法具有较强的逼近和容错能力。该算法在FLIR-ATR系统中得到应用,对于FLIR目标图像轮廓的提取和抑制杂散背景方面获得了良好的效果。

FLIR image segmentation based on wavelet neural networks with adaptive learning

In the course of tracking IR motive targets, a large number of false alarm signals might appear in the target detection stage of an IR ATR system because of the non-prediction of IR targets signature. So the false alarm signals had to be filtered in the stage of holding down background clutters. A new FLIR image segmentation technique was presented based on wavelet neural networks, aiming to fusing both local characteristic of wavelet time-frequency and adaptive learning by neural networks, and resulting in the powerful abilities of approximation and tolerate error in IR image segmentation. This new algorithm was applied in a FLIR-ATR system, and got favorable results in achieving IR target contours and damping background noises.