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Deep Learning for Satellite Image Segmentation

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

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 23196

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Special Issue Editors

Dr. M. Saquib Sarfraz

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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

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Published Papers (9 papers)

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Research

21 pages, 9403 KiB

Open AccessArticle

Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba

by Qi Zhang, Guohua Geng, Pengbo Zhou, Qinglin Liu, Yong Wang and Kang Li

Remote Sens. 2024, 16(19), 3622; https://doi.org/10.3390/rs16193622 - 28 Sep 2024

Viewed by 844

Abstract

The semantic segmentation of satellite and UAV remote sensing imagery is pivotal for address exploration, change detection, quantitative analysis and urban planning. Recent advancements have seen an influx of segmentation networks utilizing convolutional neural networks and transformers. However, the intricate geographical features and varied land cover boundary interferences in remote sensing imagery still challenge conventional segmentation networks’ spatial representation and long-range dependency capabilities. This paper introduces a novel U-Net-like network for UAV image segmentation. We developed a link aggregation Mamba at the critical skip connection stage of UNetFormer. This approach maps and aggregates multi-scale features from different stages into a unified linear dimension through four Mamba branches containing state-space models (SSMs), ultimately decoupling and fusing these features to restore the contextual relationships in the mask. Moreover, the Mix-Mamba module is incorporated, leveraging a parallel self-attention mechanism with SSMs to merge the advantages of a global receptive field and reduce modeling complexity. This module facilitates nonlinear modeling across different channels and spaces through multipath activation, catering to international and local long-range dependencies. Evaluations on public remote sensing datasets like LovaDA, UAVid and Vaihingen underscore the state-of-the-art performance of our approach. Full article

(This article belongs to the Special Issue Deep Learning for Satellite Image Segmentation)

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20 pages, 3112 KiB

Open AccessArticle

Fast Semantic Segmentation of Ultra-High-Resolution Remote Sensing Images via Score Map and Fast Transformer-Based Fusion

by Yihao Sun, Mingrui Wang, Xiaoyi Huang, Chengshu Xin and Yinan Sun

Remote Sens. 2024, 16(17), 3248; https://doi.org/10.3390/rs16173248 - 2 Sep 2024

Viewed by 988

Abstract

For ultra-high-resolution (UHR) image semantic segmentation, striking a balance between computational efficiency and storage space is a crucial research direction. This paper proposes a Feature Fusion Network (EFFNet) to improve UHR image semantic segmentation performance. EFFNet designs a score map that can be embedded into the network for training purposes, enabling the selection of the most valuable features to reduce storage consumption, accelerate speed, and enhance accuracy. In the fusion stage, we improve upon previous redundant multiple feature fusion methods by utilizing a transformer structure for one-time fusion. Additionally, our combination of the transformer structure and multibranch structure allows it to be employed for feature fusion, significantly improving accuracy while ensuring calculations remain within an acceptable range. We evaluated EFFNet on the ISPRS two-dimensional semantic labeling Vaihingen and Potsdam datasets, demonstrating that its architecture offers an exceptionally effective solution with outstanding semantic segmentation precision and optimized inference speed. EFFNet substantially enhances critical performance metrics such as Intersection over Union (IoU), overall accuracy, and F1-score, highlighting its superiority as an architectural innovation in ultra-high-resolution remote sensing image semantic segmentation. Full article

(This article belongs to the Special Issue Deep Learning for Satellite Image Segmentation)

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14 pages, 12697 KiB

Open AccessCommunication

Deep Learning-Based Detection of Oil Spills in Pakistan’s Exclusive Economic Zone from January 2017 to December 2023

by Abdul Basit, Muhammad Adnan Siddique, Salman Bashir, Ehtasham Naseer and Muhammad Saquib Sarfraz

Remote Sens. 2024, 16(13), 2432; https://doi.org/10.3390/rs16132432 - 2 Jul 2024

Viewed by 1190

Abstract

Oil spillages on a sea’s or an ocean’s surface are a threat to marine and coastal ecosystems. They are mainly caused by ship accidents, illegal discharge of oil from ships during cleaning and oil seepage from natural reservoirs. Synthetic-Aperture Radar (SAR) has proved to be a useful tool for analyzing oil spills, because it operates in all-day, all-weather conditions. An oil spill can typically be seen as a dark stretch in SAR images and can often be detected through visual inspection. The major challenge is to differentiate oil spills from look-alikes, i.e., low-wind areas, algae blooms and grease ice, etc., that have a dark signature similar to that of an oil spill. It has been noted over time that oil spill events in Pakistan’s territorial waters often remain undetected until the oil reaches the coastal regions or it is located by concerned authorities during patrolling. A formal remote sensing-based operational framework for oil spills detection in Pakistan’s Exclusive Economic Zone (EEZ) in the Arabian Sea is urgently needed. In this paper, we report the use of an encoder–decoder-based convolutional neural network trained on an annotated dataset comprising selected oil spill events verified by the European Maritime Safety Agency (EMSA). The dataset encompasses multiple classes, viz., sea surface, oil spill, look-alikes, ships and land. We processed Sentinel-1 acquisitions over the EEZ from January 2017 to December 2023, and we thereby prepared a repository of SAR images for the aforementioned duration. This repository contained images that had been vetted by SAR experts, to trace and confirm oil spills. We tested the repository using the trained model, and, to our surprise, we detected 92 previously unreported oil spill events within those seven years. In 2020, our model detected 26 oil spills in the EEZ, which corresponds to the highest number of spills detected in a single year; whereas in 2023, our model detected 10 oil spill events. In terms of the total surface area covered by the spills, the worst year was 2021, with a cumulative 395 sq. km covered in oil or an oil-like substance. On the whole, these are alarming figures. Full article

(This article belongs to the Special Issue Deep Learning for Satellite Image Segmentation)

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21 pages, 10607 KiB

Open AccessArticle

Methodology for Severe Convective Cloud Identification Using Lightweight Neural Network Model Ensembling

by Jie Zhang and Mingyuan He

Remote Sens. 2024, 16(12), 2070; https://doi.org/10.3390/rs16122070 - 7 Jun 2024

Cited by 1 | Viewed by 873

Abstract

This study introduces an advanced ensemble methodology employing lightweight neural network models for identifying severe convective clouds from FY-4B geostationary meteorological satellite imagery. We have constructed a FY-4B based severe convective cloud dataset by a combination of algorithms and expert judgment. Through the ablation study of a model ensembling combination of multiple specialized lightweight architectures—ENet, ESPNet, Fast-SCNN, ICNet, and MobileNetV2—the optimal EFNet (ENet- and Fast-SCNN-based network) not only achieves real-time processing capabilities but also ensures high accuracy in severe weather detection. EFNet consistently outperformed traditional, heavier models across several key performance indicators: achieving an accuracy of 0.9941, precision of 0.9391, recall of 0.9201, F1 score of 0.9295, and computing time of 18.65 s over the test dataset of 300 images (~0.06 s per 512 × 512 pic). ENet shows high precision but misses subtle clouds, while Fast-SCNN has high sensitivity but lower precision, leading to misclassifications. EFNet’s ensemble approach balances these traits, enhancing overall predictive accuracy. The ensemble method of lightweight models effectively aggregates the diverse strengths of the individual models, optimizing both speed and predictive performance. Full article

(This article belongs to the Special Issue Deep Learning for Satellite Image Segmentation)

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24 pages, 7257 KiB

Open AccessArticle

Radiation Feature Fusion Dual-Attention Cloud Segmentation Network

by Mingyuan He and Jie Zhang

Remote Sens. 2024, 16(11), 2025; https://doi.org/10.3390/rs16112025 - 5 Jun 2024

Cited by 1 | Viewed by 684

Abstract

In the field of remote sensing image analysis, the issue of cloud interference in high-resolution images has always been a challenging problem, with traditional methods often facing limitations in addressing this challenge. To this end, this study proposes an innovative solution by integrating radiative feature analysis with cutting-edge deep learning technologies, developing a refined cloud segmentation method. The core innovation lies in the development of FFASPPDANet (Feature Fusion Atrous Spatial Pyramid Pooling Dual Attention Network), a feature fusion dual attention network improved through atrous spatial convolution pooling to enhance the model’s ability to recognize cloud features. Moreover, we introduce a probabilistic thresholding method based on pixel radiation spectrum fusion, further improving the accuracy and reliability of cloud segmentation, resulting in the “FFASPPDANet+” algorithm. Experimental validation shows that FFASPPDANet+ performs exceptionally well in various complex scenarios, achieving a 99.27% accuracy rate in water bodies, a 96.79% accuracy rate in complex urban settings, and a 95.82% accuracy rate in a random test set. This research not only enhances the efficiency and accuracy of cloud segmentation in high-resolution remote sensing images but also provides a new direction and application example for the integration of deep learning with radiative algorithms. Full article

(This article belongs to the Special Issue Deep Learning for Satellite Image Segmentation)

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9 pages, 2189 KiB

Open AccessCommunication

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 9 | Viewed by 3139

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 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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21 pages, 9128 KiB

Open AccessArticle

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 14 | Viewed by 3245

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 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

Open AccessArticle

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 - 4 Nov 2022

Cited by 12 | Viewed by 3100

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 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

Open AccessArticle

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 - 3 Oct 2022

Cited by 13 | Viewed by 6008

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 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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