Abstract
Abstract. With new accessibility to satellite videos, retrieving the dynamic information of moving objects over a vast territory becomes possible with the development of advanced video processing and machine learning techniques. Detecting moving objects can be based on the structures of both background and foreground of a satellite video, and the background is assumed to lay in a low dimensional subspace. As the moving objects in satellite videos are groups of neighbouring pixels other than isolated pixels, Low-rank and Structured Sparse Decomposition (LSD) with structured sparsity regularization on the foreground can suppress the false alarms caused by isolated outliers. However, in LSD, the groups of neighbouring pixels are extracted by a fixed sliding window over each video frame, which ignores the coherence on the appearance of a moving object. For example, a moving object can be in an irregular shape and arbitrary orientation. In this paper, we argue that the spatial groups on the foreground can be defined using the concept of superpixels, where each superpixel is formed by a group of spatially connected similar pixels obtained from over-segmentation. We conduct low-rank matrix decomposition at superpixel level, which is named as Superpixel-based LSD (S-LSD). To handle the variation in moving objects, we combine the superpixels at a range of scales in the superpixel-based spatial regularization on the foreground. With the reduction in the number of spatial groups, S-LSD presents reduced computation complexity. The results on two satellite videos show a satisfactory performance with a significant saving in processing time when the proposed S-LSD approach is applied.
Highlights
The cube satellites Jilin-1 (Luo et al, 2017) and SkySat (Team, 2016) can produce satellite videos over a large territory
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-2-2020, 2020 XXIV ISPRS Congress (2020 edition) satellite videos, an Extended Low-rank and Structured Sparse Matrix Decomposition (E-Low-rank and Structured Sparse Decomposition (LSD)) model is proposed for boosting the Moving Object Detection (MOD) performance by imposing structured sparse regularization on the foreground (Zhang et al, 2019a,b)
We propose to conduct low-rank and structured sparse matrix decomposition with spatial groups defined by superpixels, which is named as Superpixel-based Low-rank and Structured Sparse Matrix Decomposition (S-LSD) in this paper
Summary
The cube satellites Jilin-1 (Luo et al, 2017) and SkySat (Team, 2016) can produce satellite videos over a large territory. In satellite videos where spatial resolution is low and color information is limited, these approaches have limited improvement in MOD performance, as they risk merging neighbouring targets Another set of spatial prior on the foreground is defined on the sparsity over groups of spatial neighboring pixels other than independent pixels. Satellite videos, an Extended Low-rank and Structured Sparse Matrix Decomposition (E-LSD) model is proposed for boosting the MOD performance by imposing structured sparse regularization on the foreground (Zhang et al, 2019a,b). It is reasonable that we assume a moving object is commonly composed of one or more coherent regions and each of them can be extracted by over-segmentation Inspired by this observation, we propose to conduct low-rank and structured sparse matrix decomposition with spatial groups defined by superpixels, which is named as Superpixel-based Low-rank and Structured Sparse Matrix Decomposition (S-LSD) in this paper. We compared the proposed S-LSD with the state-of-the-art algorithms on two satellite videos and the experimental results validate the significant reduced processing time by S-LSD with satisfactory MOD performance
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