Ship Detection in Highresolution Remote Sensing Images Based on Deformable Part Model

Abstract:

A method of ship detection in highresolution remote sensing images using mixture of deformable part models(DPMs) is proposed in this paper. The method is robust to size difference by constructing multiscale histogram of oriented gradients (HOG) feature pyramids. Deformable part models are introduced to deal with the deformation of the key parts of ships. And a sliding window detection strategy is adopted to separate the clustered ships. Considering that HOG features are orientationsensitive, improved training and detection methods are also proposed. In the training phase, all the samples are rotated to the same direction for parameter learning to reduce the number of templates. In the detection phase, the regions of interest are rotated to a specific direction to implement template matching. The experiments of ship detection in GaoFen2 highresolution remote sensing images are carried out. It is shown that the proposed method is effective.