Special Issue "Deep Learning-Based Cloud Detection for Remote Sensing Images"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 15 November 2021.

Special Issue Editor

Prof. Dr. Luis Gómez-Chova
E-Mail Website
Guest Editor
Image Processing Laboratory, University of Valencia, E-46100 Burjassot (Valencia), Spain
Interests: machine learning; image and signal processing; remote sensing; multispectral images; cloud detection

Special Issue Information

Dear Colleagues,

The number of Earth observation satellites is growing exponentially. However, satellite images acquired by optical sensors can be affected significantly by the presence of clouds, which can be considered as a source of uncertainty, when the objective is to study the surface, or as the signal, when studying the atmosphere. In any case, automatic detection of clouds becomes mandatory for the operational exploitation of Earth observation satellite images and further retrieval of derived bio-physical products. Moreover, classification of cloud types is also gaining importance since clouds represent one of the bigger sources of uncertainty in climate projections.

Despite the efforts in developing robust and reliable cloud detection algorithms, there are still significant deficiencies in their overall accuracy, notably false detection over bright surfaces, omissions of optically thin clouds or confusion in snow/ice and cloud classification. Standard machine learning algorithms have provided promising results in this field but they do not efficiently cope with some aspects of cloud detection in satellite images. Deep learning-based algorithms could address key challenges that require urgent attention in cloud detection for current and upcoming satellites. Novel deep learning architectures and training procedures are required to better capture the spatial and spectral properties of Earth observation satellite images. Deep learning models excel exploiting the wealth of information contained in available labeled datasets, however, the generation of reference and public multi-mission datasets of satellite images for cloud detection is a key requirement that has to be better handled by the community.

In short, this Special Issue will review the state of the art in deep learning-based cloud detection algorithms for remote sensing images. Articles covering recent research about the following topics are invited for this Special Issue:

  • New machine learning algorithms based on deep architectures for cloud detection.
  • Cloud detection applications in multispectral and high-spatial resolution sensors.
  • Deep learning-based cloud detection algorithms exploiting the temporal information.
  • Cloud type classification.
  • Cloud and cloud shadows removal from optical satellite images.
  • Transfer learning approaches across similar satellite sensors.
  • Improving domain adaptation among different satellites for cloud detection.
  • Relevant datasets particularly interesting for cloud detection.
  • Intelligent and active data labeling methods for cloud detection.
  • Search and retrieval of cloud-free satellite images.

Prof. Dr. Luis Gómez-Chova
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Cloud detection
  • Cloud removal
  • Cloud shadows
  • Cloud type classification
  • Earth observation satellites
  • Multispectral images
  • Image processing
  • Machine learning
  • Deep learning
  • Transfer learning

Published Papers (2 papers)

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Research

Article
Light-Weight Cloud Detection Network for Optical Remote Sensing Images with Attention-Based DeeplabV3+ Architecture
Remote Sens. 2021, 13(18), 3617; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183617 - 10 Sep 2021
Viewed by 348
Abstract
Clouds in optical remote sensing images cause spectral information change or loss, that affects image analysis and application. Therefore, cloud detection is of great significance. However, there are some shortcomings in current methods, such as the insufficient extendibility due to using the information [...] Read more.
Clouds in optical remote sensing images cause spectral information change or loss, that affects image analysis and application. Therefore, cloud detection is of great significance. However, there are some shortcomings in current methods, such as the insufficient extendibility due to using the information of multiple bands, the intense extendibility due to relying on some manually determined thresholds, and the limited accuracy, especially for thin clouds or complex scenes caused by low-level manual features. Combining the above shortcomings and the requirements for efficiency in practical applications, we propose a light-weight deep learning cloud detection network based on DeeplabV3+ architecture and channel attention module (CD-AttDLV3+), only using the most common red–green–blue and near-infrared bands. In the CD-AttDLV3+ architecture, an optimized backbone network-MobileNetV2 is used to reduce the number of parameters and calculations. Atrous spatial pyramid pooling effectively reduces the information loss caused by multiple down-samplings while extracting multi-scale features. CD-AttDLV3+ concatenates more low-level features than DeeplabV3+ to improve the cloud boundary quality. The channel attention module is introduced to strengthen the learning of important channels and improve the training efficiency. Moreover, the loss function is improved to alleviate the imbalance of samples. For the Landsat-8 Biome set, CD-AttDLV3+ achieves the highest accuracy in comparison with other methods, including Fmask, SVM, and SegNet, especially for distinguishing clouds from bright surfaces and detecting light-transmitting thin clouds. It can also perform well on other Landsat-8 and Sentinel-2 images. Experimental results indicate that CD-AttDLV3+ is robust, with a high accuracy and extendibility. Full article
(This article belongs to the Special Issue Deep Learning-Based Cloud Detection for Remote Sensing Images)
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Article
Benchmarking Deep Learning Models for Cloud Detection in Landsat-8 and Sentinel-2 Images
Remote Sens. 2021, 13(5), 992; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050992 - 05 Mar 2021
Cited by 1 | Viewed by 906
Abstract
The systematic monitoring of the Earth using optical satellites is limited by the presence of clouds. Accurately detecting these clouds is necessary to exploit satellite image archives in remote sensing applications. Despite many developments, cloud detection remains an unsolved problem with room for [...] Read more.
The systematic monitoring of the Earth using optical satellites is limited by the presence of clouds. Accurately detecting these clouds is necessary to exploit satellite image archives in remote sensing applications. Despite many developments, cloud detection remains an unsolved problem with room for improvement, especially over bright surfaces and thin clouds. Recently, advances in cloud masking using deep learning have shown significant boosts in cloud detection accuracy. However, these works are validated in heterogeneous manners, and the comparison with operational threshold-based schemes is not consistent among many of them. In this work, we systematically compare deep learning models trained on Landsat-8 images on different Landsat-8 and Sentinel-2 publicly available datasets. Overall, we show that deep learning models exhibit a high detection accuracy when trained and tested on independent images from the same Landsat-8 dataset (intra-dataset validation), outperforming operational algorithms. However, the performance of deep learning models is similar to operational threshold-based ones when they are tested on different datasets of Landsat-8 images (inter-dataset validation) or datasets from a different sensor with similar radiometric characteristics such as Sentinel-2 (cross-sensor validation). The results suggest that (i) the development of cloud detection methods for new satellites can be based on deep learning models trained on data from similar sensors and (ii) there is a strong dependence of deep learning models on the dataset used for training and testing, which highlights the necessity of standardized datasets and procedures for benchmarking cloud detection models in the future. Full article
(This article belongs to the Special Issue Deep Learning-Based Cloud Detection for Remote Sensing Images)
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