Missing data reconstruction in VHR images based on progressive structure prediction and texture generation

Xu, Hanwen; Tang, Xinming; Ai, Bo; Gao, Xiaoming; Yang, Fanlin; Wen, Zhen

doi:10.1016/j.isprsjprs.2020.11.020

Cited by 12 publications

(3 citation statements)

References 23 publications

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Section: A Missing Data Reconstructionmentioning

confidence: 99%

“…Large scale data missing mainly refers to the data missing phenomena caused by clouds [144], shadow and large area occlusion [141]. In reference [141], to deal with VHR images with large-scale missing regions, this paper proposed that the reconstruction process is divided into two connected parts: structure prediction and texture generation. In the first part, one generator predicts the edges of objects in missing regions; then, in the second part, the other generator predicts the textures based on edge structural information from the first part.…”

Section: A Missing Data Reconstructionmentioning

confidence: 99%

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A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

Liu¹

2021

Preprint

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In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

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Section: A Missing Data Reconstructionmentioning

confidence: 99%

Section: A Missing Data Reconstructionmentioning

confidence: 99%

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

Liu¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Typical methods include spatial interpolation (Siravenha et al, 2011;Li et al, 2014), and deep learning algorithms such as Generative Adversarial Networks (Xu et al, 2021;Zheng et al, 2021). These methods do not require additional data source, while produce unreliable results for large cloud (Cao et al, 2020;Zhang et al, 2021;Zheng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Virtual image patch-based cloud removal for Landsat images

Wang¹,

Ke²,

Zhou³

et al. 2022

Preprint

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The inevitable thick cloud contamination in Landsat images has severely limited the usability and applications of these images. Developing cloud removal algorithms has been a hot research topic in recent years. Many previous algorithms used one or multiple cloud-free images in the same area acquired on other dates as reference image(s) to reconstruct missing pixel values. Although it has been widely recognized that reference image(s) has great impacts on the performances of cloud removal algorithms, it still remains challenging to determine the optimal reference image(s). In addition, abrupt land cover change can substantially degrade the reconstruction accuracies. To address these issues, we present a new cloud removal algorithm called Virtual Image patch-based Cloud Removal (VICR). For each cloud region, VICR reconstructs the missing surface reflectance by three steps: virtual image patch construction based on time-series reference images, similar pixel selection using the newly proposed temporally weighted spectral distance (TWSD), and residual image estimation. By establishing two buffer zones around the cloud region, VICR allows automatic selection of the optimal set of time-series reference images. The effectiveness of VICR was validated at four testing sites with different landscapes (i.e., urban, croplands and wetlands) and land change patterns (i.e., phenological change, abrupt change cause by flooding and tidal inundation), and the performances were compared with mNSPI (modified neighborhood similar pixel interpolator), WLR (weighted linear regression) and ARRC (AutoRegression to Remove Clouds). Experimental results showed that VICR outperformed the other algorithms and achieved lower Root Mean Square Errors in surface reflectance estimation at the four sites. The improvement is particularly noticeable at the sites with abrupt land change. By considering the difference in the contributions from each reference image, TWSD improved the ability of VICR in predicting abrupt change in surface reflectance. Moreover, VICR is more robust to different cloud sizes and to changing reference images. VICR is also computationally faster than ARRC and mNSPI. The framework for time-series image cloud removal by VICR has great potential to be applied for large datasets processing.

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Stacked denoising autoencoder for missing traffic data reconstruction via mobile edge computing

Dai

Luo

Zhao

et al. 2023

Neural Comput & Applic

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Missing data reconstruction in VHR images based on progressive structure prediction and texture generation

Cited by 12 publications

References 23 publications

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

Virtual image patch-based cloud removal for Landsat images

Stacked denoising autoencoder for missing traffic data reconstruction via mobile edge computing

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