针对可见光航空遥感监测中，耀斑和云阴影等强噪音的干扰使水中目标很难直接发现这一问题，提出了一种基于Gabor滤波器和BP神经网络的尾迹纹理自动提取算法，通过提取它们运动产生的尾迹实现对它们的准确识别。该方法分为两步：第1步是选取等大小的含尾迹纹理的水面子图像和不含尾迹纹理的水面子图像，通过一组Gabor滤波器得到它们的特征图像，计算每个子图像特征图的均值和方差，将它们作为神经网络的训练样本对BP网络进行训练得到用于识别的网络；第2步是将待提取的整幅图像分成很多与第1步中子图像等大小的子图像，分别计算它们的Gabor特征图像，并得到它们的均值和方差，把它们作为神经网络的输入，得到它们是否是纹理区域，由整幅子图像的识别结果得到一幅二值图像，用Hough变换检测图像中的直线，根据直线的长度判断尾迹是否存在。大量的实验结果表明，该方法能够准确地提取运动目标产生的尾迹纹理。

To solve the problem that it is difficult to directly detect the object in the water due to flares and cloud shadows, this paper proposed a water wake recognition method based on Multi-Channel Gabor filters, and BP neural network. First, we select sample sub-images of same sizes with wake texture and without wake texture, then, we obtain feature images using a group of Gabor filters and calculate the mean and variance of feature images to acquive, the input vectors and train the BP network. Secondly we divide the whole image into sub-images with the same size as the first step, calculate mean and variance of Gabor feature images, caculate the input vector and judge whether the sub-image contain a wake texture by the trained BP network in the first step. We obtain a binary image by the classify results of the whole image, detect lines using Hough transform and judge whether there is a wake in the whole image. From experiment results, it is proved that the proposed algorithm can attain the wake texture precisely.