In this paper, a novel method based on PCA with shape and intensity information is proposed for infrared forward-looking
airport recognition. Here, PCA is used to perform feature transformation and airport recognition. It maps an
input image into a low-dimensional feature space in order to make the mapped features linearly separable. And the input
image of conventional method only uses intensity information. The proposed method not only considers the intensity but
also adopts shape-mask to emphasize the important object area information. The novel method is evaluated based on the
sequence of infrared forward-looking airport images by using different airport recognition methods such as BP
networking and SVM. The experiment's results have been compared based on percentage of correct classification,
computation complexity and amount of training data, which show that this new method is superior to other recognition
approach on computation complexity under almost the same recognition accuracy.
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