The detection and recognition of aircraft targets in remote sensing images has recently become a popular research issue both local and abroad. In traditional methods

invariant features are extracted from segmented targets to train learning machines. In cases with less interference

traditional methods work effectively. In practice

however

several interfering factors

including non-uniform/unstable illumination

complex background

and noise

contaminate the quality of remote sensing images. Thus

traditional methods are time-consuming and cannot achieve high recognition accuracy. To recognize an aircraft target in a remote sensing image rapidly and accurately

this study proposes an aircraft target recognition algorithm based on saliency images as well as global and local features. Visually salient targets in a remote sensing image are extracted using the modified Itti algorithm. A region increasing algorithm and a line marked algorithm are both applied to search connected regions

Accordingly

the number and location of candidate targets can be determined. Multi-scale autoconvolution (MSA)

pseudo-Zernike moment

and Harris-Laplace feature are extracted

and their stability is evaluated based the ratio of the standard deviation to the mean. The selected features are combined to constitute a feature vector. Lastly

the candidate targets are recognized via support vector machine (SVM). Experimental results show that the detection and recognition accuracies of the proposed algorithm are 97.2% and 94.9%

respectively

which are both higher than the values for existing methods. In addition

the proposed algorithm has other advantages

including low time consumption

low false alarm rate (0.03)

strong robustness against noise

complex background and affine transformation. This study proposes a new aircraft target recognition algorithm based on saliency image as well as global and local features. The combination of the employed features

including MSA

Pseudo-Zernike moment and Harris-Laplace feature

provide more discriminant information than single feature. The novel algorithm improves both recognition efficiency and anti-interference ability.