Using Deep Learning for Restoration of Precipitation Echoes in Radar Data

Lepetit, Pierre; Ly, Camille; Barthès, Laurent; Mallet, Cécile; Viltard, Nicolas; Lemaître, Yvon; Rottner, Lucie

doi:10.1109/tgrs.2021.3052582

Cited by 10 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clutter Removal & Error Detection -For weather radars in San Carlos de Bariloche, Argentina, Rosell et al ( 2020) implemented an ANN to remove clutter from precipitation products. Lepetit et al (2021), for France, devised a weakly supervised learning approach for clutter removal that enables them to avoid the problem of a lack of ground truth data. Their approach used Unet and required data from rain gauges.…”

Section: Data Augmentation and Synthesismentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

View full text Add to dashboard Cite

With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital Earth data have increased in recent years. Due to the increased availability of computer resources and the growing popularity of deep learning applications, this data has been a crucial source for data-driven studies that have transformed the fields of climate and earth science. One of the critical data sources is precipitation supporting climate and earth science studies on modeling, forecasting, and preparedness for extreme events (i.e., floods, droughts, pollution transport). In this study, we worked on an extensive review of manuscripts focusing on use of deep learning methods to tackle challenges either to improve the quality or extrapolate (forecast) rainfall datasets. The purpose of this study is to summarize the most recent developments in deep learning approaches for forecasting rainfall or improving rainfall datasets, as well as highlighting issues, shortcomings, and open questions with insightful recommendations for future directions.

show abstract

Section: Data Augmentation and Synthesismentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Research studies for better rainfall products can be broadly classified as either quality-or resolution-related. Dataset improvements using deep neural networks include data cleaning to eliminate noise (Lepetit et al, 2021), increasing the resolution or accuracy of datasets by various statistical or data-driven methods (Li et al, 2019;Demiray et al, 2021a;Demiray et al, 2021b), synthetic data generation (Gautam et al, 2020), and bias correction . Resolution-related improvements, on the other hand, either focus on increasing the resolution of two spatial dimensions or the temporal dimension.…”

Section: Related Workmentioning

confidence: 99%

TempNet – Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

Sit¹,

Seo²,

Demir³

2022

Preprint

View full text Add to dashboard Cite

The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep learning approach that augments rainfall data with increased time resolutions to complement relatively lower resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs) to improve the temporal resolution of radar-based rainfall products and compare the proposed model with an optical flow-based interpolation method and CNN-baseline model. The methodology presented in this study could be used for enhancing rainfall maps with better temporal resolution and imputation of missing frames in sequences of 2D rainfall maps to support hydrological and flood forecasting studies.

show abstract

“…Although most research papers in this field focus on sea clutter, DNNs have also been designed to address other types of clutter. For example, in [165] The DNN structures presented in [161]- [166] and their main features are summarized in TABLE 9. Note that except for the works mentioned above, deep convolutional autoencoders were proposed for target detection in sea clutter in [167], [168], and a LSTM-based network was designed for sea clutter prediction in [169].…”

Section: B Cluttermentioning

confidence: 99%

“…LSTM: (2 × LSTM + 1 × FC) ① pure noise; ② interrupted sampling repeater jamming (ISRJ); ③ aiming; ④ blocking; ⑤ sweeping; ⑥ distance deception; ⑦ dense false targets; ⑧ smart noise; ⑨ chaff; ⑩ noise amplitude modulation jamming (AM); ⑪ noise frequency modulation jamming (FM); ⑫ noise convolution jamming (CN); ⑬ noise product jamming (CP); ⑭ smeared spectrum jamming (SMSP); ⑮ chopping and interleaving jamming (C&I); ⑯ comb spectral jamming (COMB); ⑰ single frequency; ⑱ narrowband barrage; ⑲wideband barrage; ⑳ rectangular wave convolution jamming;which was tested with the micro-Doppler signatures of drones and wind-turbines measured with X-band CW radar. In[166],Lepetit et al. used U-Net, a CNN variant that was originally proposed for medical image segmentation, to remove clutter from precipitation echoes collected by weather radar.…”

mentioning

confidence: 99%

Deep-Learning for Radar: A Survey

Geng¹,

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

A comprehensive and well-structured review on the application of deep learning (DL) based algorithms, such as convolutional neural networks (CNN) and long-short term memory (LSTM), in radar signal processing is given. The following DL application areas are covered: i) radar waveform and antenna array design; ii) passive or low probability of interception (LPI) radar waveform recognition; iii) automatic target recognition (ATR) based on high range resolution profiles (HRRPs), Doppler signatures, and synthetic aperture radar (SAR) images; and iv) radar jamming/clutter recognition and suppression. Although DL is unanimously praised as the ultimate solution to many bottleneck problems in most of existing works on similar topics, both the positive and the negative sides of stories about DL are checked in this work. Specifically, two limiting factors of the real-life performance of deep neural networks (DNNs), limited training samples and adversarial examples, are thoroughly examined. By investigating the relationship between the DL-based algorithms proposed in various papers and linking them together to form a full picture, this work serves as a valuable source for researchers who are seeking potential research opportunities in this promising research field.

show abstract

Using Deep Learning for Restoration of Precipitation Echoes in Radar Data

Cited by 10 publications

References 27 publications

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

TempNet – Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

Deep-Learning for Radar: A Survey

Contact Info

Product

Resources

About