Recent Advances in Intelligent Processing of Satellite Video: Challenges, Methods, and Applications

Li, Shengyang; Sun, Xian; Gu, Yanfeng; Lv, Yixuan; Zhao, Manqi; Zhuang, Zhitao; Guo, Weilong; Sun, Y.; Wang, Han; Yang, Jian

doi:10.1109/jstars.2023.3296451

Cited by 12 publications

(2 citation statements)

References 108 publications

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“…According to the amount of manually labeled data used, video object segmentation models can be divided into four categories [37] : fully supervised video object segmentation methods, unsupervised video segmentation methods, weakly supervised video segmentation methods, and semi-supervised video segmentation methods. Fully supervised methods are not suitable for satellite video object segmentation due to the considerable amount of manually labeled data required and the extremely high cost.…”

Section: A Semi-supervised Video Object Segmentation Methodsmentioning

confidence: 99%

BSVOS: Background Interference Suppression Strategy for Satellite Video Multiobject Segmentation

Kou,

Li,

Yang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Video satellites provide dynamic real-time monitoring of hotspot areas and objects by continuously imaging objects within a specified sequence, providing dynamic information over large areas. Satellite video object segmentation aims to separate foreground and background continuously. Compared with satellite video single-object segmentation, satellite video multi-object segmentation can separate multiple foreground objects of interest in the scene simultaneously. So far, the research on multi-object segmentation mainly focuses on extracting deeper informative features. However, this does not cope well with the problems of weak object texture information, foreground-background disproportion, and small object scale in satellite videos. Therefore, this paper proposes an end-to-end semi-supervised segmentation network named background interference suppression strategy for satellite video multi-object segmentation (BSVOS), which is the first research about satellite video multi-object segmentation. First, spatial and temporal matching templates integrate spatio-temporal information to enhance texture features within a sequence. Second, a novel matching strategy is also proposed to suppress complex background interference in satellite video. Finally, both intuitive and quantitative experimental results indicate that our proposed method outperforms other multi-object segmentation models.

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Section: A Semi-supervised Video Object Segmentation Methodsmentioning

confidence: 99%

BSVOS: Background Interference Suppression Strategy for Satellite Video Multiobject Segmentation

Kou,

Li,

Yang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Another literature review is [34]. The authors of this publication highlighted the difficulties and challenges of enhancing the resolution of the sequences of images acquired by satellites.…”

Section: Related Workmentioning

confidence: 99%

Modified ESRGAN with Uformer for Video Satellite Imagery Super-Resolution

Karwowska,

Wierzbicki

2024

Remote Sensing

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In recent years, a growing number of sensors that provide imagery with constantly increasing spatial resolution are being placed on the orbit. Contemporary Very-High-Resolution Satellites (VHRS) are capable of recording images with a spatial resolution of less than 0.30 m. However, until now, these scenes were acquired in a static way. The new technique of the dynamic acquisition of video satellite imagery has been available only for a few years. It has multiple applications related to remote sensing. However, in spite of the offered possibility to detect dynamic targets, its main limitation is the degradation of the spatial resolution of the image that results from imaging in video mode, along with a significant influence of lossy compression. This article presents a methodology that employs Generative Adversarial Networks (GAN). For this purpose, a modified ESRGAN architecture is used for the spatial resolution enhancement of video satellite images. In this solution, the GAN network generator was extended by the Uformer model, which is responsible for a significant improvement in the quality of the estimated SR images. This enhances the possibilities to recognize and detect objects significantly. The discussed solution was tested on the Jilin-1 dataset and it presents the best results for both the global and local assessment of the image (the mean values of the SSIM and PSNR parameters for the test data were, respectively, 0.98 and 38.32 dB). Additionally, the proposed solution, in spite of the fact that it employs artificial neural networks, does not require a high computational capacity, which means it can be implemented in workstations that are not equipped with graphic processors.

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Tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features

Sharma,

Srivastava

2024

Lecture Notes in Electrical Engineering

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Recent Advances in Intelligent Processing of Satellite Video: Challenges, Methods, and Applications

Cited by 12 publications

References 108 publications

BSVOS: Background Interference Suppression Strategy for Satellite Video Multiobject Segmentation

BSVOS: Background Interference Suppression Strategy for Satellite Video Multiobject Segmentation

Modified ESRGAN with Uformer for Video Satellite Imagery Super-Resolution

Tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features

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