Satellite Video Object Detection Based on Enhanced 3DTV Regularization and Gaussian Prior

Qian Yin, Wei An, Ting Liu, Yang Sun, Zaiping Lin, Yulan Guo, Hanyun Wang

Abstract: Satellite videos have played important roles in many applications in recent years due to the advantages of continuous providing high temporal resolution remote sensing images. Although much progress has been achieved for moving object detection (MOD) in satellite videos, the low-rank characteristics of background and the intensity variations of moving objects across frames have not been fully exploited. In this article, we propose an efficient method for MOD in satellite videos, which models the background with enhanced 3-D total variation (E-3DTV) regularization and the moving objects with Gaussian prior. Specifically, considering that the gradient maps on the spatial and temporal dimensions exhibit different physical meanings, we model the background with different Laplacian sparsity priors for the gradient maps along the spatial and temporal dimensions for 3DTV regularization. Different from current methods, which model moving objects with sparsity characteristics in each frame alone, we utilize Gaussian prior to model intensity changing characteristics of moving objects across frames. After integrating background model and moving object model into low-rank sparse matrix factorization framework, the alternating direction method of multipliers (ADMM) is adopted to iteratively optimize the parameters of background and moving object models. We conduct experiments on VISO and SkySat datasets, and the results demonstrate that our method achieves superior MOD performance with high computational efficiency compared to state-of-the-art methods.

External IDs:dblp:journals/tgrs/YinALSLGW25