CBEN provides paired optical-radar images with cloud occlusion, revealing 23-33 point AP drops in clear-sky trained models and 17-29 point relative gains when models are trained on cloudy data.
A Remote Sensing Image Dataset for Cloud Removal
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Cloud-based overlays are often present in optical remote sensing images, thus limiting the application of acquired data. Removing clouds is an indispensable pre-processing step in remote sensing image analysis. Deep learning has achieved great success in the field of remote sensing in recent years, including scene classification and change detection. However, deep learning is rarely applied in remote sensing image removal clouds. The reason is the lack of data sets for training neural networks. In order to solve this problem, this paper first proposed the Remote sensing Image Cloud rEmoving dataset (RICE). The proposed dataset consists of two parts: RICE1 contains 500 pairs of images, each pair has images with cloud and cloudless size of 512*512; RICE2 contains 450 sets of images, each set contains three 512*512 size images. , respectively, the reference picture without clouds, the picture of the cloud and the mask of its cloud. The dataset is freely available at \url{https://github.com/BUPTLdy/RICE_DATASET}.
citation-role summary
dataset 1
citation-polarity summary
years
2026 4roles
dataset 1polarities
use dataset 1representative citing papers
TGPNet unifies denoising, cloud removal, shadow removal, deblurring, and SAR despeckling into one model via task-guided prompting and reports state-of-the-art results on a new multi-modal benchmark.
ATT-CR introduces triangular attention and feature-selected gating to reduce computational cost and cloudy-pixel interference in remote sensing cloud removal.
IncepDeHazeGAN is a GAN with Inception blocks and multi-layer feature fusion that claims state-of-the-art single-image dehazing performance on satellite datasets.
citing papers explorer
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CBEN -- A Multimodal Machine Learning Dataset for Cloud Robust Remote Sensing Image Understanding
CBEN provides paired optical-radar images with cloud occlusion, revealing 23-33 point AP drops in clear-sky trained models and 17-29 point relative gains when models are trained on cloudy data.
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Task-Guided Prompting for Unified Remote Sensing Image Restoration
TGPNet unifies denoising, cloud removal, shadow removal, deblurring, and SAR despeckling into one model via task-guided prompting and reports state-of-the-art results on a new multi-modal benchmark.
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ATT-CR: Adaptive Triangular Transformer for Cloud Removal
ATT-CR introduces triangular attention and feature-selected gating to reduce computational cost and cloudy-pixel interference in remote sensing cloud removal.
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IncepDeHazeGAN: Novel Satellite Image Dehazing
IncepDeHazeGAN is a GAN with Inception blocks and multi-layer feature fusion that claims state-of-the-art single-image dehazing performance on satellite datasets.