Airplane recognition system for visual sensor networks.

This paper established aircraft models by using 3D-MAX and designed a three-dimensional the aircraft target image library. In this image library, divided different aircraft models into three categories, each category were divided into 24 groups according to flight poses. It discussed the three types of aircraft image texture feature extraction methods. Then it calculated contrast fusion value, entropy of fusion, inverse gap value feature fusion of each type of aircraft image by multi-sensor consistency fusion method based on closeness, and pre-judge the test object classification by the minimum distance method. It also introduced K-L transform, feature subspace and projection coefficient vector, calculated the coefficient vector of each image's projection on gallery feature subspace, and tested image's projection on feature subspace. Then it introduced fisher optimal discriminate vector and discriminate rules. It considered coefficient vector of image projection on feature subspace as an object; derived optimal discriminate vectors through training each set of image's positive samples and negative samples. It judged test target's category and flight attitude through fisher discriminate rules. It proved a high recognition rate of the recognition system by experiments, as well as the judgment of the aircraft complex flight attitude. [ABSTRACT FROM AUTHOR]