GAN-Based Satellite Imaging: A Survey on Techniques and Applications

Mansourifar, Hadi; Moskovitz, Alexander H.; Klingensmith, Ben; Mintas, Dino; Simske, Steve

doi:10.1109/access.2022.3221123

Cited by 13 publications

(4 citation statements)

References 92 publications

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“…Synthetic Datasets Goodfellow et al ( 2014) proposed GAN as a new generative modeling framework [14] to synthesize new data with the same characteristics from training examples, visually approximating the training data set. Various GANbased methods have been proposed for image synthesis in recent years [15], [16], [17], [18], [19], [20], [21], [22], [23], and [24] with applications spreading rapidly from computer vision and machine learning communities to domain-specific areas such as medical [25] [26], [27], [28], [29], and remote sensing [30], [31], [32] [33], [34], [35], [36], [37], [38], [39], [40], and [41]; industrial process [42], [43], [44], [45], [46], [47], and [48]; and agriculture [49], [50], [51], [52].…”

Section: B Gan (Generative Adversarial Network) To Producementioning

confidence: 99%

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Aji,

Bin Ghazali,

Akbar

2023

Journal of Robotics and Control

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Identifying Unstriped Bunches (USB) is a pivotal challenge in palm oil production, contributing to reduced mill efficiency. Existing manual detection methods are proven time-consuming and prone to inaccuracies. Therefore, we propose an innovative solution harnessing computer vision technology. Specifically, we leverage the Faster R-CNN (Region-based Convolution Neural Network), a robust object detection algorithm, and complement it with Progressive Growing Generative Adversarial Networks (PGGAN) for synthetic image generation. Nevertheless, a scarcity of authentic USB images may hinder the application of Faster R-CNN. Herein, PGGAN is assumed to be pivotal in generating synthetic images of Empty Fruit Bunches (EFB) and USB. Our approach pairs synthetic images with authentic ones to train the Faster R-CNN. The VGG16 feature generator serves as the architectural backbone, fostering enhanced learning. According to our experimental results, USB detectors that were trained solely with authentic images resulted in an accuracy of 77.1%, which highlights the potential of this methodology. However, employing solely synthetic images leads to a slightly reduced accuracy of 75.3%. Strikingly, the fusion of authentic and synthetic images in a balanced ratio of 1:1 fuels a remarkable accuracy surge to 87.9%, signifying a 10.1% improvement. This innovative amalgamation underscores the potential of synthetic data augmentation in refining detection systems. By amalgamating authentic and synthetic data, we unlock a novel dimension of accuracy in USB detection, which was previously unattainable. This contribution holds significant implications for the industry, ensuring further exploration into advanced data synthesis techniques and refining detection models.

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Section: B Gan (Generative Adversarial Network) To Producementioning

confidence: 99%

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Aji,

Bin Ghazali,

Akbar

2023

Journal of Robotics and Control

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“…Adversarial image synthesis [6], [8], [9], [14], [16] is one of the hot topics in AI, with many successful use cases being reported. However, the Partial Pixel-wise Annotation (PPA) problem has never been investigated in the field of Conditional Generative Adversarial Nets (CGANs).…”

Section: Introductionmentioning

confidence: 99%

Towards CGAN-based Satellite Image Synthesis with Partial Pixel-Wise Annotation

Mansourifar¹,

Simske²

2023

Preprint

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Conditional Generative Adversarial Nets (CGANs) need a significantly huge dataset with a detailed pixel-wise annotation to generate high-quality images. Unfortunately, any amount of missing pixel annotations may significantly impact the result not only locally, but also in annotated areas. To the best of our knowledge, such a challenge has never been investigated in the broader field of GANs. In this paper, we take the first step in this direction to study the problem of CGAN-based satellite image synthesis given partially annotated images. We first define the problem of image synthesis using partially annotated data, and we discuss a scenario in which we face such a challenge. We then propose an effective solution called detail augmentation to address this problem. To do so, we tested two different approaches to augment details to compensate for missing pixel-wise annotations. In the first approach, we augmented the original images with their Canny edges to using the CGAN to compensate for the missing annotations. The second approach, however, attempted to assign a color to all pixels with missing annotation. Eventually, a different CGAN was trained to translate the new feature images into a final output.

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“…The Generative Adversarial Network (GAN) [9] is a well-known example of generative modelling that is often used to generate time-series data and images. GANs are used in many applications including in health care [10], sequence creation [11], image processing [12], satellite applications [13] etc. we teach deep learning models with synthetic ECG signals representing different arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

A Generative Adversarial Network Based Approach for Synthesis of Deep Fake Electrocardiograms

Rayavarapu

Prashanthi

Kumar³

et al. 2023

IJRITCC

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Analyzing the data from an electrocardiogram (ECG) can reveal important details about a patient's heart health. A key component of modern medicine is the use of AI and ML-based computer-aided diagnosis tools to aid in making life-or-death decisions. It is common practice to use them in cardiology for the automatic early diagnosis of a variety of potentially fatal illnesses. The machine learning algorithm's need for a large amount of training data to build the learning model is an empirical challenge in the medical domain. To address this challenge, study into methods for creating synthetic patient data has blossomed. There is a higher risk of privacy invasion due to the need for massive amounts of training data for deep learning automated medical diagnostic systems that may help assess the state of the heart from this signal. To combat this issue, researchers have tried to create artificial ECG readings by analyzing only the statistical distributions of the accessible authentic training data.The primary goal of this study is to learn how generative adversarial networks can be used to create artificial ECG signals for use as training data in a classification task. In this study, we used both GAN and WGAN for generation of artificial ECG signals.

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GAN-Based Satellite Imaging: A Survey on Techniques and Applications

Cited by 13 publications

References 92 publications

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Towards CGAN-based Satellite Image Synthesis with Partial Pixel-Wise Annotation

A Generative Adversarial Network Based Approach for Synthesis of Deep Fake Electrocardiograms

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