A deep network approach to multitemporal cloud detection

• URL: http://arxiv.org/abs/2012.10393v1
• Date: Wed, 9 Dec 2020 08:58:36 GMT
• Title: A deep network approach to multitemporal cloud detection
• Authors: Devis Tuia, Benjamin Kellenberger, Adrian P\'erez-Suay, Gustau Camps-Valls
• Abstract summary: We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure.
• Score: 15.39911641413792
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.

Related papers

• HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation [106.09886920774002]
  We present a hybrid-view-based knowledge distillation framework, termed HVDistill, to guide the feature learning of a point cloud neural network. Our method achieves consistent improvements over the baseline trained from scratch and significantly out- performs the existing schemes.
  arXiv  Detail & Related papers  (2024-03-18T14:18:08Z)
• Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos [75.9251839023226]
  We propose a Masked-temporal Structure Prediction (MaST-Pre) method to capture the structure of point cloud videos without human annotations. MaST-Pre consists of two self-supervised learning tasks. First, by reconstructing masked point tubes, our method is able to capture appearance information of point cloud videos. Second, to learn motion, we propose a temporal cardinality difference prediction task that estimates the change in the number of points within a point tube.
  arXiv  Detail & Related papers  (2023-08-18T02:12:54Z)
• U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series [22.39321609253005]
  We develop a neural model that can map a cloud-masked input sequence to a cloud-free output sequence. We experimentally evaluate the proposed model on a dataset of satellite Sentinel-2 time series acquired all over Europe. Compared to a standard baseline, it increases the PSNR by 1.8 dB at previously seen locations and by 1.3 dB at unseen locations.
  arXiv  Detail & Related papers  (2023-05-22T17:37:10Z)
• Cloud Classification with Unsupervised Deep Learning [6.285964948191585]
  Our framework learns cloud features directly from radiance data produced by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument. We present preliminary results showing that our method extracts physically relevant information from radiance data and produces meaningful cloud classes.
  arXiv  Detail & Related papers  (2022-09-30T16:56:58Z)
• Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey [104.71816962689296]
  Unsupervised point cloud representation learning has attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using deep neural networks.
  arXiv  Detail & Related papers  (2022-02-28T07:46:05Z)
• Sentinel-1 and Sentinel-2 Spatio-Temporal Data Fusion for Clouds Removal [51.9654625216266]
  A novel method for clouds-corrupted optical image restoration has been presented and developed based on a joint data fusion paradigm. It is worth highlighting that the Sentinel code and the dataset have been implemented from scratch and made available to interested research for further analysis and investigation.
  arXiv  Detail & Related papers  (2021-06-23T08:15:01Z)
• Seeing Through Clouds in Satellite Images [14.84582204034532]
  This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultra/super-high frequency band that penetrate clouds to help reconstruct the occluded regions in multispectral images.
  arXiv  Detail & Related papers  (2021-06-15T20:01:27Z)
• TPCN: Temporal Point Cloud Networks for Motion Forecasting [47.829152433166016]
  We propose a novel framework with joint spatial and temporal learning for trajectory prediction. In the spatial dimension, agents can be viewed as an unordered point set, and thus it is straightforward to apply point cloud learning techniques to model agents' locations. Experiments on the Argoverse motion forecasting benchmark show that our approach achieves the state-of-the-art results.
  arXiv  Detail & Related papers  (2021-03-04T14:44:32Z)
• Generating the Cloud Motion Winds Field from Satellite Cloud Imagery Using Deep Learning Approach [1.8655840060559172]
  We explore the cloud motion winds algorithm based on data-driven deep learning approach. We use deep learning model to automatically learn the motion feature representations and directly output the field of cloud motion winds. We also try to use a single cloud imagery to predict the cloud motion winds field in a fixed region, which is impossible to achieve using traditional algorithms.
  arXiv  Detail & Related papers  (2020-10-03T05:40:36Z)
• Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision [68.35777836993212]
  We propose a Pseudo-LiDAR point cloud network to generate temporally and spatially high-quality point cloud sequences. By exploiting the scene flow between point clouds, the proposed network is able to learn a more accurate representation of the 3D spatial motion relationship.
  arXiv  Detail & Related papers  (2020-06-20T03:11:04Z)

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.