A shadow constrained conditional generative adversarial net for SRTM data restoration

Dong, Guoshuai; Huang, Weimin; Smith, William A. P.; Ren, Peng

doi:10.1016/j.rse.2019.111602

Cited by 32 publications

(14 citation statements)

References 41 publications

(44 reference statements)

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“…The output of GFCM also suffers when the removed object is large in size. Dong et al [23] synthesize high-quality results for filling voids of radar data. They use a shadow constrained conditional GAN network to restore the damaged region.…”

Section: Related Workmentioning

confidence: 99%

A Novel GAN-Based Network for Unmasking of Masked Face

et al. 2020

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Recent deep learning based image editing methods have achieved promising results for removing object in an image but fail to generate plausible results for removing large objects of complex nature, especially in facial images. The objective of this work is to remove mask objects in facial images. This problem is challenging because (1) most of the time facial masks cover quite a large region of face that even extends beyond the actual face boundary below chin, and (2) facial image pairs with and without mask object do not exist for training. We break the problem into two stages: mask object detection and image completion of the removed mask region. The first stage of our model automatically produces binary segmentation for the mask region. Then, the second stage removes the mask and synthesizes the affected region with fine details while retaining the global coherency of face structure. For this, we have employed a GAN-based network using two discriminators where one discriminator helps learn the global structure of the face and then another discriminator comes in to focus learning on the deep missing region. To train our model in a supervised manner, we create a paired synthetic dataset using publicly available CelebA dataset and evaluated on real world images collected from the Internet. Our model outperforms others representative state-of-the-art approaches both qualitatively and quantitatively. INDEX TERMS Generative adversarial network, object removal, image editing.

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Section: Related Workmentioning

confidence: 99%

A Novel GAN-Based Network for Unmasking of Masked Face

et al. 2020

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“…The missing data of voids in mountainous areas by Shuttle Radar Topography Mission (SRTM) are small and medium-scale data missing. The authors in [138] incorporated the shadow geometric constraints into the CGAN. The shadow boundary loss function, shadow ceiling loss function, and shadow entrance curvature loss function are combined with an adversarial loss to guide the generator to well predict the value in void areas.…”

Section: A Missing Data Reconstructionmentioning

confidence: 99%

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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“…However, the TTGM also requires a post-processing step that uses Poisson blur to fuse the resulting filled surface with the original data. Dong et al [38] used terrain shading as a constraint for conditional generation adversarial networks for filling DEM voids. However, this method has high time and labor costs due to use the filling of data based on field surveys and in situ measurements to train the model.…”

Section: Introductionmentioning

confidence: 99%

Voids Filling of DEM with Multiattention Generative Adversarial Network Model

et al. 2022

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The digital elevation model (DEM) acquired through photogrammetry or LiDAR usually exposes voids due to phenomena such as instrumentation artifact, ground occlusion, etc. For this reason, this paper proposes a multiattention generative adversarial network model to fill the voids. In this model, a multiscale feature fusion generation network is proposed to initially fill the voids, and then a multiattention filling network is proposed to recover the detailed features of the terrain surrounding the void area, and the channel-spatial cropping attention mechanism module is proposed as an enhancement of the network. Spectral normalization is added to each convolution layer in the discriminator network. Finally, the training of the model by a combined loss function, including reconstruction loss and adversarial loss, is optimized. Three groups of experiments with four different types of terrains, hillsides, valleys, ridges and hills, are conducted for validation of the proposed model. The experimental results show that (1) the structural similarity surrounding terrestrial voids in the three types of terrains (i.e., hillside, valley, and ridge) can reach 80–90%, which implies that the DEM accuracy can be improved by at least 10% relative to the traditional interpolation methods (i.e., Kriging, IDW, and Spline), and can reach 57.4%, while other deep learning models (i.e., CE, GL and CR) only reach 43.2%, 17.1% and 11.4% in the hilly areas, respectively. Therefore, it can be concluded that the structural similarity surrounding the terrestrial voids filled using the model proposed in this paper can reach 60–90% upon the types of terrain, such as hillside, valley, ridge, and hill.

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A shadow constrained conditional generative adversarial net for SRTM data restoration

Cited by 32 publications

References 41 publications

A Novel GAN-Based Network for Unmasking of Masked Face

A Novel GAN-Based Network for Unmasking of Masked Face

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

Voids Filling of DEM with Multiattention Generative Adversarial Network Model

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