Abstract
Low-rank matrix decomposition approaches have achieved significant progress in small and dim object detection in satellite videos. However, it is still challenging to achieve robust performance and fast processing under complex and highly heterogeneous backgrounds since satellite video data can neither adequately fit the foreground structure nor the background model in the existing matrix decomposition models. In this letter, we propose a novel object detection method based on a spatial–temporal tensor data structure. First, we construct a tensor data structure to exploit the inner spatial and temporal correlation within a satellite video. Second, we extend the decomposition formulation with bounded noise to achieve robust performance under complex backgrounds. This formulation integrates low-rank background, structured sparse foreground, and their noises into a tensor decomposition problem. For background separation, a weighted Schatten <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -norm is incorporated to provide adaptive threshold to obtain the singular value of the background tensor. Finally, the proposed model is solved using the alternative direction method of multipliers (ADMM) scheme. Experimental results on various real scenes demonstrate the superiority of the proposed method against the compared approaches.
Highlights
A S a new earth observation technology, satellite video is able to provide a period of continuous observation over an area, providing rich dynamic information of an object, such as the moving trajectory, speed and directions
Zhang et al [6], [8] proposed several methods based on the Low-rank and Structured Sparse Decomposition (LSD) framework [5] to achieve moving object detection in satellite videos
Videos 003-009 are provided by Algorithm 1: The process of weighted Schatten p-norm minimization (WSNM)-STTN
Summary
A S a new earth observation technology, satellite video is able to provide a period of continuous observation over an area, providing rich dynamic information of an object, such as the moving trajectory, speed and directions. Ahmadi et al [3] employed a median background model to detect objects and used the nearest neighbor algorithm to produce trajectories These statistical methods do not consider the structure knowledge of an video (e.g., temporal similarity of background and spatial contiguity of foreground). Zhang et al [6], [8] proposed several methods based on the Low-rank and Structured Sparse Decomposition (LSD) framework [5] to achieve moving object detection in satellite videos. These matrix RPCA based methods can only convert the videos with a natural 3D structure to a 2D data, which can destroy the structure information and reduce the detection performance. Extensive experiments have demonstrated the superiority of our WSNM-STTN to the state-of-the-art methods
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