目的 传统的飞机目标识别算法一般是通过目标分割，然后提取不变特征进行训练来完成目标的识别。但是，对于实际情况比较复杂的遥感图像飞机目标，至今没有一种适合多种机型的分割识别算法。针对现有识别算法的不足，提出一种基于特征点空间分布、颜色不变矩和Zernike不变矩相结合的遥感图像飞机目标识别算法。方法 首先，对预处理后的遥感图像和模板图像进行小波变换，在低分辨率图像下采用圆投影特征进行粗匹配，确定候选目标；粗匹配结束后，提取高分辨率图像的多尺度Harris-Laplace角点，并画出Delaunay三角网，同时提取出颜色不变矩和Zernike不变矩；然后使用欧氏距离作为这3种特征的相似性度量，并和样本图像进行加权匹配；最后选取欧氏距离最小的图像作为最终的识别目标。结果 实验结果表明，本文算法飞机检测精度比现有算法高2.2%，飞机识别精度比现有算法高1.4%10.4%。该算法能从遥感图像中精确识别出十大飞机目标，并对背景、噪声、视角变化等多种干扰具有良好的鲁棒性。结论 提出了一种基于特征点空间分布、颜色不变矩和Zernike不变矩相结合的飞机识别算法，该算法使用了图像的多种信息，包括特征点和不变矩，有效地克服了使用单一特征无法描述多种信息的不足。实验结果表明，本文采用基于特征点和不变矩的飞机识别算法比其他算法具有更强的抗干扰能力和识别精度。

Objective Many algorithms for aircraft recognition have been develop during the recent years. Traditional aircraft recognition algorithms are generally extracting the invariant features to train the learning machine after the targets segmentation, such as SVM, neural networks, and others. After training progress, the machine can recognize aircraft targets automatically. If there is less interference, traditional algorithm can work well. However, in actual circumstances, there are a lot of disturbances in the remote sensing images, such as noise, shadow, tanks, trees, etc. At this time, traditional algorithm cannot work well. However, for complex remote aircraft images, there is not a suitable segmentation algorithm for various aircraft models, so traditional recognition algorithm is not universal. Aiming at the deficiencies of the existing recognition algorithms, we propose an aircraft recognition method for remote sensing images based on the distribution of the feature points, color invariant moments, and Zernike invariant moments. Method Above all, preprocessing for remote sensing images and module images should be done. Preprocessing concludes a graying and noise-removing process. The graying process is making colorful images become gray images, which can draw Harris-Laplace corners and Zernike moments in the following process. After that, the next process is the noise-removal. Noise-removal is a very important procedure. If there is much noise in the gray images, the moments extracted from the images will not be stable. A wavelet is a wave shape, which has a small area, finite length, and zero mean value. By changing the coefficients, wavelet transform can refine the input images with multiple-scales, and it can analyze signals in the time domain and the frequency domain at the same time, so it is called a mathematical microscope. When the preprocessing is done, the remote sensing images and module images are transformed by a wavelet. In the low-resolution images, use ring projection for rough matching. In the high-resolution images, we extract multi-scale corners using Harris-Laplace, and triangulate use Delaunay-triangulation. At the same time, abstract color invariant moments and Zernike moments. Afterwards, take the Euclidean distance as the similarity measure for the three different features, and use these features to match the sample images with the weighted coefficients. At last, we select the sample image, which has minimum Euclidean distance to the finally recognized target. Result After experiment, the results show that aircraft detection accuracy in this paper is 2.2% higher than recent algorithms, and aircraft recognition accuracy is 1.4%10.4% higher than recent algorithms. This algorithm can accurately identify ten aircraft targets in remote sensing images, and has good robustness to background, noise, viewpoint changes, and other interference. Conclusion In this paper, we propose a new aircraft target recognition algorithm based on the spatial distribution of feature points, color invariant moments, and Zernike invariant moments. There are three different features in this algorithm, containing feature points and invariant moments, which include diverse information of the images. Therefore, the new algorithm overcomes the shortcoming that the single feature cannot contain enough information. The experimental results show that the new aircraft target recognition algorithm in this paper has better anti-interference ability and recognition accuracy than other algorithms.