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Remote Sensing
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Deep Learning for Satellite Image Segmentation
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Deep Learning for Satellite Image Segmentation

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 13872

Special Issue Editors

Dr. M. Saquib Sarfraz

E-Mail Website
Guest Editor
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
Interests: deep learning; image and video understanding; human-computer interaction
Dr. Muhammad Adnan Siddique

E-Mail Website
Guest Editor
Remote Sensing and Spatial Analytics Lab, Information Technology University of the Punjab (ITU), Lahore 54000, Pakistan
Interests: earth observation and remote sensing; SAR image processing; AI and machine learning

Special Issue Information

Dear Colleagues,

Satellite-based imaging has grown exponentially over the last few decades. Each year, several new imaging sensors are launched, and therefore, a rapidly growing amount of big imaging data is being produced. Improved methods to reap information from these data must be developed, as well as methods to contextualize these data in accordance with the relevant remote sensing applications. A key image processing step in this regard is image segmentation, which plays a central role in several applications, ranging from automated land cover classification to change detection. Instead of pixel-based and object-based classification, a recent paradigm shift means that the image segmentation is now commonly carried out via deep learning. This method allows deep feature extraction over the imagery. Recent advancements have been made in the application of deep learning in multiple imaging modalities, such as spaceborne synthetic aperture radars (SARs) and multispectral and hyperspectral optical sensors. Nonetheless, several challenges still need to be addressed, particularly in the context of weakly annotated training sets, class imbalances, deep learning architectures for multilayered time series image stacks, multiclass classifications alongside instance segmentation, segmentation over big remote sensing data, etc.

The aim of this Special Issue is to solicit scientific and technological advancements in the form of original research articles related to the use of deep learning for the segmentation of spaceborne imagery, for radar as well as optical sensors. This issue falls within the scope of the MDPI journal Remote Sensing, as image segmentation is one of the key steps in various remote sensing applications, and deep learning is pertinent given the growing rise in the use of machine-learning-based approaches to address various problems in the automated pixel-wise classification of big remote sensing data.

Research articles may address, but are not limited, to the following themes related to the use of deep learning in satellite image segmentation:

  • Land cover or land use in natural terrain or urban centers;
  • Change detection in urban zones and sprawl delineation and building footprint assessments;
  • Change detection over natural terrain (agriculture, wetland, high mountain or marine contexts);
  • Segmentation in the context of multimodal data or feature fusion;
  • Deep learning architectures, training and validation improvements;
  • Benchmark dataset creation.

Dr. M. Saquib Sarfraz
Dr. Muhammad Adnan Siddique
Guest Editors

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 submissions that pass pre-check are 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 2700 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

  • deep learning
  • spaceborne imaging
  • image segmentation
  • semantic segmentation
  • instance segmentation
  • data fusion in remote sensing
  • satellite imaging
  • spaceborne optical imaging
  • change detection

Published Papers (4 papers)

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Research

9 pages, 2189 KiB  
Communication
The Application of a Convolutional Neural Network for the Detection of Contrails in Satellite Imagery
by Jay P. Hoffman, Timothy F. Rahmes, Anthony J. Wimmers and Wayne F. Feltz
Remote Sens. 2023, 15(11), 2854; https://doi.org/10.3390/rs15112854 - 31 May 2023
Cited by 6 | Viewed by 2306
Abstract
This study presents a novel approach for the detection of contrails in satellite imagery using a convolutional neural network (CNN). Contrails are important to monitor because their contribution to climate change is uncertain and complex. Contrails are found to have a net warming [...] Read more.
This study presents a novel approach for the detection of contrails in satellite imagery using a convolutional neural network (CNN). Contrails are important to monitor because their contribution to climate change is uncertain and complex. Contrails are found to have a net warming effect because the clouds prevent terrestrial (longwave) radiation from escaping the atmosphere. Globally, this warming effect is greater than the cooling effect the clouds have in the reduction of solar (shortwave) radiation reaching the surface during the daytime. The detection of contrails in satellite imagery is challenging due to their similarity to natural clouds. In this study, a certain type of CNN, U-Net, is used to perform image segmentation in satellite imagery to detect contrails. U-Net can accurately detect contrails with an overall probability of detection of 0.51, a false alarm ratio of 0.46 and a F1 score of 0.52. These results demonstrate the effectiveness of using a U-Net for the detection of contrails in satellite imagery and could be applied to large-scale monitoring of contrail formation to measure their impact on climate change. Full article
(This article belongs to the Special Issue Deep Learning for Satellite Image Segmentation)
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Graphical abstract

21 pages, 9128 KiB  
Article
A Dynamic Effective Class Balanced Approach for Remote Sensing Imagery Semantic Segmentation of Imbalanced Data
by Zheng Zhou, Change Zheng, Xiaodong Liu, Ye Tian, Xiaoyi Chen, Xuexue Chen and Zixun Dong
Remote Sens. 2023, 15(7), 1768; https://doi.org/10.3390/rs15071768 - 25 Mar 2023
Cited by 11 | Viewed by 2228
Abstract
The wide application and rapid development of satellite remote sensing technology have put higher requirements on remote sensing image segmentation methods. Because of its characteristics of large image size, large data volume, and complex segmentation background, not only are the traditional image segmentation [...] Read more.
The wide application and rapid development of satellite remote sensing technology have put higher requirements on remote sensing image segmentation methods. Because of its characteristics of large image size, large data volume, and complex segmentation background, not only are the traditional image segmentation methods difficult to apply effectively, but the image segmentation methods based on deep learning are faced with the problem of extremely unbalanced data between categories. In order to solve this problem, first of all, according to the existing effective sample theory, the effective sample calculation method in the context of semantic segmentation is firstly proposed in the highly unbalanced dataset. Then, a dynamic weighting method based on the effective sample concept is proposed, which can be applied to the semantic segmentation of remote sensing images. Finally, the applicability of this method to different loss functions and different network structures is verified on the self-built Landsat8-OLI remote sensing image-based tri-classified forest fire burning area dataset and the LoveDA dataset, which is for land-cover semantic segmentation. It has been concluded that this weighting algorithm can enhance the minimal-class segmentation accuracy while ensuring that the overall segmentation performance in multi-class segmentation tasks is verified in two different semantic segmentation tasks, including the land use and land cover (LULC) and the forest fire burning area segmentation In addition, this proposed method significantly improves the recall of forest fire burning area segmentation by as much as about 30%, which is of great reference value for forest fire research based on remote sensing images. Full article
(This article belongs to the Special Issue Deep Learning for Satellite Image Segmentation)
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25 pages, 9217 KiB  
Article
DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images
by Zhiheng Liu, Xuemei Chen, Suiping Zhou, Hang Yu, Jianhua Guo and Yanming Liu
Remote Sens. 2022, 14(21), 5567; https://doi.org/10.3390/rs14215567 - 04 Nov 2022
Cited by 7 | Viewed by 2303
Abstract
Water body segmentation is an important tool for the hydrological monitoring of the Earth. With the rapid development of convolutional neural networks, semantic segmentation techniques have been used on remote sensing images to extract water bodies. However, some difficulties need to be overcome [...] Read more.
Water body segmentation is an important tool for the hydrological monitoring of the Earth. With the rapid development of convolutional neural networks, semantic segmentation techniques have been used on remote sensing images to extract water bodies. However, some difficulties need to be overcome to achieve good results in water body segmentation, such as complex background, huge scale, water connectivity, and rough edges. In this study, a water body segmentation model (DUPnet) with dense connectivity and multi-scale pyramidal pools is proposed to rapidly and accurately extract water bodies from Gaofen satellite and Landsat 8 OLI (Operational Land Imager) images. The proposed method includes three parts: (1) a multi-scale spatial pyramid pooling module (MSPP) is introduced to combine shallow and deep features for small water bodies and to compensate for the feature loss caused by the sampling process; (2) dense blocks are used to extract more spatial features to DUPnet’s backbone, increasing feature propagation and reuse; (3) a regression loss function is proposed to train the network to deal with the unbalanced dataset caused by small water bodies. The experimental results show that the F1, MIoU, and FWIoU of DUPnet on the 2020 Gaofen dataset are 97.67%, 88.17%, and 93.52%, respectively, and on the Landsat River dataset, they are 96.52%, 84.72%, 91.77%, respectively. Full article
(This article belongs to the Special Issue Deep Learning for Satellite Image Segmentation)
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20 pages, 4483 KiB  
Article
Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer
by Weitao Li, Hui Gao, Yi Su and Biffon Manyura Momanyi
Remote Sens. 2022, 14(19), 4942; https://doi.org/10.3390/rs14194942 - 03 Oct 2022
Cited by 9 | Viewed by 4917
Abstract
With the development of deep learning, the performance of image semantic segmentation in remote sensing has been constantly improved. However, the performance usually degrades while testing on different datasets because of the domain gap. To achieve feasible performance, extensive pixel-wise annotations are acquired [...] Read more.
With the development of deep learning, the performance of image semantic segmentation in remote sensing has been constantly improved. However, the performance usually degrades while testing on different datasets because of the domain gap. To achieve feasible performance, extensive pixel-wise annotations are acquired in a new environment, which is time-consuming and labor-intensive. Therefore, unsupervised domain adaptation (UDA) has been proposed to alleviate the effort of labeling. However, most previous approaches are based on outdated network architectures that hinder the improvement of performance in UDA. Since the effects of recent architectures for UDA have been barely studied, we reveal the potential of Transformer in UDA for remote sensing with a self-training framework. Additionally, two training strategies have been proposed to enhance the performance of UDA: (1) Gradual Class Weights (GCW) to stabilize the model on the source domain by addressing the class-imbalance problem; (2) Local Dynamic Quality (LDQ) to improve the quality of the pseudo-labels via distinguishing the discrete and clustered pseudo-labels on the target domain. Overall, our proposed method improves the state-of-the-art performance by 8.23% mIoU on Potsdam→Vaihingen and 9.2% mIoU on Vaihingen→Potsdam and facilitates learning even for difficult classes such as clutter/background. Full article
(This article belongs to the Special Issue Deep Learning for Satellite Image Segmentation)
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