Zou Huanxin

A fast ship detection algorithm in SAR imagery for wide area ocean surveillance

Ship detection is a basic problem to be solved in SAR application of ocean surveillance. To deal with the rapid increasing SAR data, fast algorithm has become a hot topic in the research of ship detection. This paper proposes a fast ship detection algorithm in SAR imagery for wide area ocean surveillance. The algorithm adopts global two-parameter CFAR and adaptive CFAR based K distribution in the coarse and fine detection phase separately. Performances on detection accuracy and efficiency have been analyzed theoretically. Validation results on several space-born SAR images illustrate the fast method can preserve detection accuracy and improve the calculation efficiency.

Layover and shadow detection based on distributed spaceborne single-baseline InSAR

Distributed spaceborne single-baseline InSAR is an effective technique to get high quality Digital Elevation Model. Layover and Shadow are ubiquitous phenomenon in SAR images because of geometric relation of SAR imaging. In the signal processing of single-baseline InSAR, the phase singularity of Layover and Shadow leads to the phase difficult to filtering and unwrapping. This paper analyzed the geometric and signal model of the Layover and Shadow fields. Based on the interferometric signal autocorrelation matrix, the paper proposed the signal number estimation method based on information theoretic criteria, to distinguish Layover and Shadow from normal InSAR fields. The effectiveness and practicability of the method proposed in the paper are validated in the simulation experiments and theoretical analysis.

Combing rough set and RBF neural network for large-scale ship recognition in optical satellite images

Large scale ship recognition in optical remote sensing images is of great importance for many military applications. It aims to recognize the category information of the detected ships for effective maritime surveillance. The contributions of the paper can be summarized as follows: Firstly, based on the rough set theory, the common discernibility degree is used to compute the significance weight of each candidate feature and select valid recognition features automatically; Secondly, RBF neural network is constructed based on the selected recognition features. Experiments on recorded optical satellite images show the proposed method is effective and can get better classification rates at a higher speed than the state of the art methods.

A method to detect layover and shadow based on distributed spaceborne single-baseline InSAR

Layover and Shadow are inevitable phenomenena in InSAR, which seriously destroy the continuity of interferometric phase images and present difficulties in the follow-up phase unwrapping. Thus, its significant to detect layover and shadow. This paper presents an approach to detect layover and shadow using the auto-correlation matrix and amplitude of the two images. The method can make full use of the spatial information of neighboring pixels and effectively detect layover and shadow regions in the case of low registration accuracy. Experiment result on the simulated data verifies effectiveness of the algorithm.