This study proposes a target detection approach based on the target existence probability in complex scenes of a synthetic aperture radar image. Superpixels are the basic unit throughout the approach and are labelled into each classified scene by a texture feature. The original and predicted saliency depth values for each scene are derived through self-information of all the labelled superpixels in each scene. Thereafter, the target existence probability is estimated based on the comparison of two saliency depth values. Lastly, an improved visual attention algorithm, in which the scenes of the saliency map are endowed with different weights related to the existence probabilities, derives the target detection result. This algorithm enhances the attention for the scene that contains the target. Hence, the proposed approach is self-adapting for complex scenes and the algorithm is substantially suitable for different detection missions as well (e.g. vehicle, ship or aircraft detection in the related scenes of road, harbour or airport, respectively). Experimental results on various data show the effectiveness of the proposed method.
['Shuo Liu', 'Zongjie Cao', 'Honggang Wu', 'Yiming Pi', 'Haiyi Yang']
Target detection in complex scene of SAR image based on existence probability