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Recent Trends of Generative Adversarial Networks (GANs) in Remote Sensing Applications

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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 (30 April 2023) | Viewed by 13076

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Special Issue Editors

Dr. Wadii Boulila

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Guest Editor

PSU Faculty, Prince Sultan University, Riyadh 12435, Saudi Arabia
Interests: remote sensing; big data; deep learning; internet of things; uncertainty modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Maha Driss

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Guest Editor

PSU Faculty, Prince Sultan University, Riyadh 12435, Saudi Arabia
Interests: remote sensing; deep learning; Internet of Things; service computing

Prof. Dr. Anis Koubaa

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Guest Editor

PSU Faculty, Prince Sultan University, Riyadh 12435, Saudi Arabia
Interests: Internet of Things; unmanned aerial vehicles; wireless sensor networks; mobile robots; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Akrem Sellami

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Lorraine Research Laboratory, Lorraine University, 54506 Vandœuvre-lès-Nancy, France
Interests: deep learning; dimensionality reduction; computer vision; remote sensing

Prof. Dr. Imed Riadh Farah

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Guest Editor

Campus Universitaire de La Manouba, University of Manouba, Manouba 2010, Tunisia
Interests: remote sensing; artificial intelligence; pattern recognition

Special Issue Information

Dear Colleagues,

Generative adversarial networks (GANs) have recently been attracting extensive attention from the remote sensing (RS) community as an emerging field of deep learning. GANs are neural network architectures that introduce the training process as a competition between a generator and a discriminator to produce new data conforming to learned patterns. GANs are able to learn interpretable representations from RS data without supervision, which makes them useful for many use cases, such as data generation or augmentation, super resolution, panchromatic sharpening, haze removal and restoration, and cloud removal. In the literature, some recent works are proposed to provide theoretical and technical GANs-based approaches in many fields related to RS.

This timely Special Issue invites original contributions on a range of related topics, including but not limited to:

Theoretical analysis and foundations of GANs and their variants in RS fields;
Generative and discriminative models for representation learning of multimodal RS data;
Novel formulations (network structures, objective functions, feature learning, etc.) of GANs;
New training methods of GANs with application to RS fields;
Real-world applications of GANs in RS fields, such as agriculture, environment, hydrology, forest management, regional planning, climate, land use/cover, urban, etc.

Dr. Wadii Boulila
Dr. Maha Driss
Prof. Dr. Anis Koubaa
Dr. Akrem Sellami
Prof. Dr. Imed Riadh Farah
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.

Published Papers (5 papers)

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20 pages, 2323 KiB

Open AccessArticle

A Hybrid Approach Based on GAN and CNN-LSTM for Aerial Activity Recognition

by Abir Bousmina, Mouna Selmi, Mohamed Amine Ben Rhaiem and Imed Riadh Farah

Remote Sens. 2023, 15(14), 3626; https://doi.org/10.3390/rs15143626 - 21 Jul 2023

Cited by 5 | Viewed by 2768

Abstract

Unmanned aerial vehicles (UAVs), known as drones, have played a significant role in recent years in creating resilient smart cities. UAVs can be used for a wide range of applications, including emergency response, civil protection, search and rescue, and surveillance, thanks to their high mobility and reasonable price. Automatic recognition of human activity in aerial videos captured by drones is critical for various tasks for these applications. However, this is difficult due to many factors specific to aerial views, including camera motion, vibration, low resolution, background clutter, lighting conditions, and variations in view. Although deep learning approaches have demonstrated their effectiveness in a variety of challenging vision tasks, they require either a large number of labelled aerial videos for training or a dataset with balanced classes, both of which can be difficult to obtain. To address these challenges, a hybrid data augmentation method is proposed which combines data transformation with the Wasserstein Generative Adversarial Network (GAN)-based feature augmentation method. In particular, we apply the basic transformation methods to increase the amount of video in the database. A Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model is used to learn the spatio-temporal dynamics of actions, then a GAN-based technique is applied to generate synthetic CNN-LSTM features conditioned on action classes which provide a high discriminative spatio-temporal features. We tested our model on the YouTube aerial database, demonstrating encouraging results that surpass those of previous state-of-the-art works, including an accuracy rate of 97.83%. Full article

(This article belongs to the Special Issue Recent Trends of Generative Adversarial Networks (GANs) in Remote Sensing Applications)

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19 pages, 6240 KiB

Open AccessArticle

Temporal Co-Attention Guided Conditional Generative Adversarial Network for Optical Image Synthesis

by Yongchun Weng, Yong Ma, Fu Chen, Erping Shang, Wutao Yao, Shuyan Zhang, Jin Yang and Jianbo Liu

Remote Sens. 2023, 15(7), 1863; https://doi.org/10.3390/rs15071863 - 31 Mar 2023

Cited by 1 | Viewed by 1396

Abstract

In the field of SAR-to-optical image synthesis, current methods based on conditional generative adversarial networks (CGANs) have satisfying performance under simple scenarios, but the performance drops severely under complicated scenarios. Considering that SAR images can form a robust time series due to SAR’s all-weather imaging ability, we take advantage of this and extract a temporal correlation from bi-temporal SAR images to guide the translation. To achieve this, we introduce a co-attention mechanism into the CGAN that learns the correlation between optically-available and optically-absent time points, selectively enhances the features of the former time point, and eventually guides the model to a better optical image synthesis on the latter time point. Additionally, we adopt a strategy to balance the weight of optical and SAR features to extract better features from the SAR input. With these strategies, the quality of synthesized images is notably improved in complicated scenarios. The synthesized images can increase the spatial and temporal resolution of optical imagery, greatly improving the availability of data for the applications of crop monitoring, change detection, and visual interpretation. Full article

(This article belongs to the Special Issue Recent Trends of Generative Adversarial Networks (GANs) in Remote Sensing Applications)

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25 pages, 2978 KiB

Open AccessArticle

A Hybrid Privacy-Preserving Deep Learning Approach for Object Classification in Very High-Resolution Satellite Images

by Wadii Boulila, Manel Khazri Khlifi, Adel Ammar, Anis Koubaa, Bilel Benjdira and Imed Riadh Farah

Remote Sens. 2022, 14(18), 4631; https://doi.org/10.3390/rs14184631 - 16 Sep 2022

Cited by 18 | Viewed by 2382

Abstract

Deep learning (DL) has shown outstanding performances in many fields, including remote sensing (RS). DL is turning into an essential tool for the RS research community. Recently, many cloud platforms have been developed to provide access to large-scale computing capacity, consequently permitting the usage of DL architectures as a service. However, this opened the door to new challenges associated with the privacy and security of data. The RS data used to train the DL algorithms have several privacy requirements. Some of them need a high level of confidentiality, such as satellite images related to public security with high spatial resolutions. Moreover, satellite images are usually protected by copyright, and the owner may strictly refuse to share them. Therefore, privacy-preserving deep learning (PPDL) techniques are a possible solution to this problem. PPDL enables training DL on encrypted data without revealing the original plaintext. This study proposes a hybrid PPDL approach for object classification for very-high-resolution satellite images. The proposed encryption scheme combines Paillier homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). This combination aims to enhance the encryption of satellite images while ensuring a good runtime and high object classification accuracy. The method proposed to encrypt images is maintained through the public keys of PHE and SHE. Experiments were conducted on real-world high-resolution satellite images acquired using the SPOT6 and SPOT7 satellites. Four different CNN architectures were considered, namely ResNet50, InceptionV3, DenseNet169, and MobileNetV2. The results showed that the loss in classification accuracy after applying the proposed encryption algorithm ranges from 2% to 3.5%, with the best validation accuracy on the encrypted dataset reaching 92%. Full article

(This article belongs to the Special Issue Recent Trends of Generative Adversarial Networks (GANs) in Remote Sensing Applications)

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22 pages, 11996 KiB

Open AccessArticle

Adversarial Representation Learning for Hyperspectral Image Classification with Small-Sized Labeled Set

by Shuhan Zhang, Xiaohua Zhang, Tianrui Li, Hongyun Meng, Xianghai Cao and Li Wang

Remote Sens. 2022, 14(11), 2612; https://doi.org/10.3390/rs14112612 - 29 May 2022

Cited by 6 | Viewed by 1842

Abstract

Hyperspectral image (HSI) classification is one of the main research contents of hyperspectral technology. Existing HSI classification algorithms that are based on deep learning use a large number of labeled samples to train models to ensure excellent classification effects, but when the labeled samples are insufficient, the deep learning model is prone to overfitting. In practice, there are a large number of unlabeled samples that have not been effectively utilized, so it is meaningful to study a semi-supervised method. In this paper, an adversarial representation learning that is based on a generative adversarial networks (ARL-GAN) method is proposed to solve the small samples problem in hyperspectral image classification by applying GAN to the representation learning domain in a semi-supervised manner. The proposed method has the following distinctive advantages. First, we build a hyperspectral image block generator whose input is the feature vector that is extracted from the encoder and use the encoder as a feature extractor to extract more discriminant information. Second, the distance of the class probability output by the discriminator is used to measure the error between the generated image block and the real image instead of the root mean square error (MSE), so that the encoder can extract more useful information for classification. Third, GAN and conditional entropy are used to improve the utilization of unlabeled data and solve the small sample problem in hyperspectral image classification. Experiments on three public datasets show that the method achieved better classification accuracy with a small number of labeled samples compared to other state-of-the-art methods. Full article

(This article belongs to the Special Issue Recent Trends of Generative Adversarial Networks (GANs) in Remote Sensing Applications)

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14 pages, 2850 KiB

Open AccessTechnical Note

An Unpaired Thermal Infrared Image Translation Method Using GMA-CycleGAN

by Shihao Yang, Min Sun, Xiayin Lou, Hanjun Yang and Hang Zhou

Remote Sens. 2023, 15(3), 663; https://doi.org/10.3390/rs15030663 - 22 Jan 2023

Cited by 8 | Viewed by 2325

Abstract

Automatically translating chromaticity-free thermal infrared (TIR) images into realistic color visible (CV) images is of great significance for autonomous vehicles, emergency rescue, robot navigation, nighttime video surveillance, and many other fields. Most recent designs use end-to-end neural networks to translate TIR directly to CV; however, compared to these networks, TIR has low contrast and an unclear texture for CV translation. Thus, directly translating the TIR temperature value of only one channel to the RGB color value of three channels without adding additional constraints or semantic information does not handle the one-to-three mapping problem between different domains in a good way, causing the translated CV images not only to have blurred edges but also color confusion. As for the methodology of the work, considering that in the translation from TIR to CV the most important process is to map information from the temperature domain into the color domain, an improved CycleGAN (GMA-CycleGAN) is proposed in this work in order to translate TIR images to grayscale visible (GV) images. Although the two domains have different properties, the numerical mapping is one-to-one, which reduces the color confusion caused by one-to-three mapping when translating TIR to CV. Then, a GV-CV translation network is applied to obtain CV images. Since the process of decomposing GV images into CV images is carried out in the same domain, edge blurring can be avoided. To enhance the boundary gradient between the object (pedestrian and vehicle) and the background, a mask attention module based on the TIR temperature mask and the CV semantic mask is designed without increasing the network parameters, and it is added to the feature encoding and decoding convolution layers of the CycleGAN generator. Moreover, a perceptual loss term is applied to the original CycleGAN loss function to bring the translated images closer to the real images regarding the space feature. In order to verify the effectiveness of the proposed method, the FLIR dataset is used for experiments, and the obtained results show that, compared to the state-of-the-art model, the subjective quality of the translated CV images obtained by the proposed method is better, as the objective evaluation metric FID (Fréchet inception distance) is reduced by 2.42 and the PSNR (peak signal-to-noise ratio) is improved by 1.43. Full article

(This article belongs to the Special Issue Recent Trends of Generative Adversarial Networks (GANs) in Remote Sensing Applications)

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