Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra

Wu, Mingliang; Bu, Yude; Pan, Jingchang; Yi, Zhenping; Kong, Xiaoming

doi:10.1109/access.2020.3000174

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…In a final step, the pure spectra are reconstructed by the generator. Recent studies indicate that the proposed approach is better than those of denoising CNNs [89].…”

Section: Ai-enabled Learning Techniques For Generating Soil Spatial Productsmentioning

confidence: 96%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

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“…In a final step, the pure spectra are reconstructed by the generator. Recent studies indicate that the proposed approach is better than those of denoising CNNs [89].…”

Section: Ai-enabled Learning Techniques For Generating Soil Spatial Productsmentioning

confidence: 96%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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“…The authors of Spectra-GAN [131] designed their algorithm for spectral denoising. Their algorithm is based on CycleGAN i.e.…”

Section: E Astronomymentioning

confidence: 99%

“…Applications in Astronomy GAN models Image to image translation RadioGAN [121],pix2pix [6],pix2pixHD [21] Image data generation and augmentation SGAN [126], DCGAN [5], ProGAN [4], ExoGAN [129] Image denoising Spectra-GAN [131] • Super Resolution: HRPGAN [135] uses a PatchGAN inspired architecture to convert low resolution remote sensing images to high resolution images. The authors did not use batch normalization to preserve textures and sharp edges of ground objects in remote sensing images.…”

Section: F 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

Dash,

Ye,

Wang

2024

IEEE Access

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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 zerosum 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 INDEX TERMS deep learning, generative adversarial networks, computer vision, time series, applications

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“…The authors of Spectra-GAN [177] designed their algorithm for spectral denoising. Their algorithm is based on CycleGAN i.e.…”

Section: Astronomymentioning

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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Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra

Cited by 7 publications

References 33 publications

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing

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

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