Thick Cloud Removal from Remote Sensing Images Using Double Shift Networks

Because of the presence of clouds, the available information in optical remote sensing images is greatly reduced. These temporal-based methods are widely used for cloud removal. However, the temporal differences in multitemporal images have consistently been a challenge for these types of methods. Towards this end, a bishift network (BSN) model is proposed to remove thick clouds from optical remote sensing images. As its name implies, BSN is combined of two dependent shifts. Moment matching (MM) and deep style transfer (DST) are the first shift to preliminarily eliminate temporal differences in multitemporal images. In the second shift, an improved shift net is proposed to reconstruct missing information under cloud covers. It introduces multiscale feature connectivity with shift connections and depthwise separable convolution (DSC), which can capture local details and global semantics effectively. Through experiments with Sentinel-2 images, it has been demonstrated that the proposed BSN has great advantages over traditional methods and state-of-the-art methods in cloud removal.

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“…BSN is a further improvement on our previous research [30]. It consists of two shifts, as shown in Figure 1.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Before BSN, general fast style transfer had been used for temporal diference elimination [30]. However, regular style transfer methods are not suitable for realistic style and complex transfers.…”

Section: Deep Style Transfermentioning

confidence: 99%

Bishift Networks for Thick Cloud Removal with Multitemporal Remote Sensing Images

Long

Jing

et al. 2023

International Journal of Intelligent Systems

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A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information

Zhang,

Zhang

et al. 2024

Remote Sensing

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Reconstructing cloud-covered regions in remote sensing (RS) images holds great promise for continuous ground object monitoring. A novel lightweight machine-learning method for cloud removal constrained by conditional information (SMLP-CR) is proposed. SMLP-CR constructs a multilayer perceptron with a presingle-connection layer (SMLP) based on multisource conditional information. The method employs multi-scale mean filtering and local neighborhood sampling to gain spatial information while also taking into account multi-spectral and multi-temporal information as well as pixel similarity. Meanwhile, the feature importance from the SMLP provides a selection order for conditional information—homologous images are prioritized over images from the same season as the restoration image, and images with close temporal distances rank last. The results of comparative experiments indicate that SMLP-CR shows apparent advantages in terms of visual naturalness, texture continuity, and quantitative metrics. Moreover, compared with popular deep-learning methods, SMLP-CR samples locally around cloud pixels instead of requiring a large cloud-free training area, so the samples show stronger correlations with the missing data, which demonstrates universality and superiority.

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Thick Cloud Removal from Remote Sensing Images Using Double Shift Networks

Cited by 2 publications

References 15 publications

Bishift Networks for Thick Cloud Removal with Multitemporal Remote Sensing Images

Bishift Networks for Thick Cloud Removal with Multitemporal Remote Sensing Images

A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information

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