Airport Detection Based on Superpixel Segmentation and Saliency Analysis for Remote Sensing Images

Abstract

Traditional target detection methods are usually based on prior knowledge by template matching and classification. Nowadays, remote sensing images contain richer and richer information. It will cause high computation complexity if we still apply traditional target detection methods to remote sensing images. This paper proposes an airport detection model based on superpixel segmentation and saliency analysis. First, the input image is segmented into superpixels. Then saliency analysis is performed by calculating differences between superpixels and corresponding weights in R, G and B color channels to get the saliency map. Finally we utilize the limitation in the ratio of perimeter and area and morphology operation to eliminate the interference. Experiments compare the proposed model with three saliency analysis models qualitatively and quantitatively. Results show that the proposed model is better than the three comparative models in keeping clear boundaries, eliminating interference and maintaining intact targets.