Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network

• URL: http://arxiv.org/abs/2009.13015v2
• Date: Sat, 14 Nov 2020 08:17:05 GMT
• Title: Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
• Authors: Heng Pan
• Abstract summary: We propose a model named spatial attention generative adversarial network (SpA GAN) to solve the remote sensing imagery cloud removal task. SpA GAN imitates the human visual mechanism, and recognizes and focuses the cloud area with local-to-global spatial attention.
• Score: 0.9746724603067647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties. However, remote sensing imagery is inevitably affected by climate, especially clouds. Removing the cloud in the high-resolution remote sensing satellite image is an indispensable pre-processing step before analyzing it. For the sake of large-scale training data, neural networks have been successful in many image processing tasks, but the use of neural networks to remove cloud in remote sensing imagery is still relatively small. We adopt generative adversarial network to solve this task and introduce the spatial attention mechanism into the remote sensing imagery cloud removal task, proposes a model named spatial attention generative adversarial network (SpA GAN), which imitates the human visual mechanism, and recognizes and focuses the cloud area with local-to-global spatial attention, thereby enhancing the information recovery of these areas and generating cloudless images with better quality...

Related papers

• Attentive Contextual Attention for Cloud Removal [16.273117614996586]
  Cloud cover can significantly hinder the use of remote sensing images for Earth observation. Deep learning strategies have shown strong potential in restoring cloud-obscured areas. We introduce a novel approach named Attentive Contextual Attention (AC-Attention)
  arXiv  Detail & Related papers  (2024-11-20T05:16:31Z)
• IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images [55.40601468843028]
  We present an iterative diffusion process for cloud removal (IDF-CR) IDF-CR is divided into two-stage models that address pixel space and latent space. In the latent space stage, the diffusion model transforms low-quality cloud removal into high-quality clean output.
  arXiv  Detail & Related papers  (2024-03-18T15:23:48Z)
• Volcanic ash delimitation using Artificial Intelligence based on Pix2Pix [0.0]
  Volcanic eruptions emit ash that can be harmful to human health and cause damage to infrastructure, economic activities and the environment. The delimitation of ash clouds allows to know their behavior and dispersion, which helps in the prevention and mitigation of this phenomenon. The present work proposes the use of the Pix2Pix model, a type of generative adversarial network that learns the mapping of input images to output images.
  arXiv  Detail & Related papers  (2023-07-24T17:49:04Z)
• Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection [21.028664417133793]
  We propose to jointly tune adversarial exposure and additive perturbation for attack and constrain image close to cloudy image as Adversarial Cloud. DefenseNet can defend the proposed Adversarial Cloud in white-box setting and other attack methods in black-box setting.
  arXiv  Detail & Related papers  (2023-06-30T07:06:13Z)
• SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images [91.20864037082863]
  We propose a spatial attention weighted unmixing network, dubbed as SAWU-Net, which learns a spatial attention network and a weighted unmixing network in an end-to-end manner. In particular, we design a spatial attention module, which consists of a pixel attention block and a window attention block to efficiently model pixel-based spectral information and patch-based spatial information. Experimental results on real and synthetic datasets demonstrate the better accuracy and superiority of SAWU-Net.
  arXiv  Detail & Related papers  (2023-04-22T05:22:50Z)
• Ponder: Point Cloud Pre-training via Neural Rendering [93.34522605321514]
  We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural encoders. The learned point-cloud can be easily integrated into various downstream tasks, including not only high-level rendering tasks like 3D detection and segmentation, but low-level tasks like 3D reconstruction and image rendering.
  arXiv  Detail & Related papers  (2022-12-31T08:58:39Z)
• MM811 Project Report: Cloud Detection and Removal in Satellite Images [0.0]
  We aim to address cloud removal from satellite images using AttentionGAN. We compare our results by reproducing the results obtained using traditional GANs and auto-encoders. The outcome of this project can be used to develop applications that require cloud-free satellite images.
  arXiv  Detail & Related papers  (2022-12-21T21:14:35Z)
• Visual Attention Network [90.0753726786985]
  We propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention. We also introduce a novel neural network based on LKA, namely Visual Attention Network (VAN) VAN outperforms the state-of-the-art vision transformers and convolutional neural networks with a large margin in extensive experiments.
  arXiv  Detail & Related papers  (2022-02-20T06:35:18Z)
• Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal [5.753245638190626]
  thin cloud removal is an indispensable procedure to enhance the utilization of remote sensing images. We propose a Multi-Head Linear Attention Generative Adversarial Network (MLAGAN) for Thin Cloud Removal. Compared with six deep learning-based thin cloud removal benchmarks, the experimental results on the RICE1 and RICE2 datasets demonstrate that the proposed framework MLA-GAN has dominant advantages in thin cloud removal.
  arXiv  Detail & Related papers  (2020-12-20T11:50:54Z)
• Single Image Deraining via Scale-space Invariant Attention Neural Network [58.5284246878277]
  We tackle the notion of scale that deals with visual changes in appearance of rain steaks with respect to the camera. We propose to represent the multi-scale correlation in convolutional feature domain, which is more compact and robust than that in pixel domain. In this way, we summarize the most activated presence of feature maps as the salient features.
  arXiv  Detail & Related papers  (2020-06-09T04:59:26Z)
• Neural Sparse Representation for Image Restoration [116.72107034624344]
  Inspired by the robustness and efficiency of sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks. Our method structurally enforces sparsity constraints upon hidden neurons. Experiments show that sparse representation is crucial in deep neural networks for multiple image restoration tasks.
  arXiv  Detail & Related papers  (2020-06-08T05:15:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.