Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks

Hu, Anna; Xie, Zhen; Xu, Yongyang; Xie, Mingyu; Wu, Liang; Qiu, Qinjun

doi:10.3390/rs12244162

Cited by 28 publications

(18 citation statements)

References 50 publications

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“…To solve the fact that haze significantly reduces the accuracy of image interpretation, Ref. [61] proposes a novel unsupervised method to improve image clarity during the daytime. The method is based on cycle generative adversarial networks called the edgesharpening cycle-consistent adversarial network (ES-CCGAN).…”

Section: Learning-based Methodsmentioning

confidence: 99%

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Miclea

Ungureanu

Sandru

et al. 2021

Sensors

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In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc.

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Section: Learning-based Methodsmentioning

confidence: 99%

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Miclea

Ungureanu

Sandru

et al. 2021

Sensors

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“…• Haze removal and Restoration: Edge-sharpening cycle-consistent adversarial network (ES-CCGAN) [64] is a GAN-based unsupervised remote sensing image dehazing method based on the CycleGAN [197]. The authors used the unpaired image-to-image translation techniques for performing image dehazing.…”

Section: Remote Sensingmentioning

confidence: 99%

A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing

Dash¹,

Ye²,

Wang³

2021

Preprint

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We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications.GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs.Then we present how GAN and its variants can be applied in twelve domains, ranging from STEM fields, such as astronomy and biology, to business fields, such as marketing and finance, and to arts, such as music. As a result, researchers from other fields may grasp how GANs work and apply them to their own study. To the best of our knowledge, this article provides the most comprehensive survey of GAN's applications in different field. CCS Concepts: • General and reference → Surveys and overviews; • Computing methodologies → Computer vision; Neural networks; • Theory of computation → Machine learning theory.

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“…In contrast to traditional multi-spectral remote sensing technology, it can obtain rich oil-spill spectral characteristic information [28][29][30]. Moreover, deep learning has developed rapidly in recent years because of its powerful ability to extract features from high-dimensional data [31][32][33][34][35][36][37]. Deep networks and multi-level features fusion method for deep learning have been applied to hyperspectral image classification, and research progress has been made [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

Jiang

Zhang

et al. 2021

Remote Sensing

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Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

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Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks

Cited by 28 publications

References 50 publications

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing

Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

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