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. 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. 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. 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.