[논문]Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks

Tran, Quang-Khai; Song, Sa-kwang

doi:10.3390/atmos10050244

Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks 원문보기

Atmosphere, v.10 no.5, 2019년, pp.244 -

Tran, Quang-Khai , Song, Sa-kwang

Abstract ▼ AI-Helper

This paper presents a viewpoint from computer vision to the radar echo extrapolation task in the precipitation nowcasting domain. Inspired by the success of some convolutional recurrent neural network models in this domain, including convolutional LSTM, convolutional GRU and trajectory GRU, we designed a new sequence-to-sequence neural network structure to leverage these models in a realistic data context. In this design, we decreased the numbers of channels in high abstract recurrent layers rather than increasing them. We formulated the task as a problem of encoding five radar images and predicting 10 steps ahead at the pixel level, and found that using only the common mean squared error can misguide the training and mislead the testing. Especially, the image quality of last predictions usually degraded rapidly. As a solution, we employed some visual image quality assessment techniques including Structural Similarity (SSIM) and multi-scale SSIM to train our models. Experimental results show that our structure was more tolerant to increasing uncertainty in the data, and the use of image quality metrics can significantly reduce the blurry image issue. Moreover, we found that using SSIM was very effective and a combination of SSIM with mean squared error and mean absolute error yielded the best prediction quality.

참고문헌 (39)

Yu, W., Nakakita, E., Kim, S., Yamaguchi, K.. Improvement of rainfall and flood forecasts by blending ensemble NWP rainfall with radar prediction considering orographic rainfall. Journal of hydrology, vol.531, no.2, 494-507.

상세보기
Li, Ling, He, Zhengwei, Chen, Sheng, Mai, Xiongfa, Zhang, Asi, Hu, Baoqing, Li, Zhi, Tong, Xinhua. Subpixel-Based Precipitation Nowcasting with the Pyramid Lucas-Kanade Optical Flow Technique. Atmosphere, vol.9, no.7, 260-.

상세보기
10.1007/978-3-319-73603-7_2
Akbari Asanjan, Ata, Yang, Tiantian, Hsu, Kuolin, Sorooshian, Soroosh, Lin, Junqiang, Peng, Qidong. Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks. Journal of geophysical research. Atmospheres, vol.123, no.22,

상세보기
10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2

상세보기
10.1175/1520-0450(2004)043<0074:SDOTPO>2.0.CO;2

상세보기
10.1175/1520-0450(2004)043<0231:POPFCR>2.0.CO;2

상세보기
Lee, Hee Choon, Lee, Yong Hee, Ha, Jong-Chul, Chang, Dong-Eon, Bellon, Aldo, Zawadzki, Isztar, Lee, Gyuwon. McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part II: Real-time verification for the summer season. Asia-Pacific journal of atmospheric sciences, vol.46, no.3, 383-391.

상세보기
Tang, Jingyin, Matyas, Corene. A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar. Atmosphere, vol.9, no.5, 200-.

상세보기
10.1007/978-3-030-01424-7
Woo, Wang-chun, Wong, Wai-kin. Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting. Atmosphere, vol.8, no.3, 48-.

상세보기
Kim Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events 2018
Hang Zhao, Gallo, Orazio, Frosio, Iuri, Kautz, Jan. Loss Functions for Image Restoration With Neural Networks. IEEE transactions on computational imaging, vol.3, no.1, 47-57.

상세보기
10.1080/19479832.2016.1273259
Yang, Jingxiang, Zhao, Yongqiang, Yi, Chen, Chan, Jonathan Cheung-Wai. No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning. Remote sensing, vol.9, no.4, 305-.

상세보기
Lee Stochastic Adversarial Video Prediction arXiv 2018
CIKM AnalytiCup 2017 Dataset https://tianchi.aliyun.com/dataset/dataDetail?dataId=1085
Wang, Zhou, Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.13, no.4, 600-612.

상세보기
Zhou Wang, Bovik, A.C.. Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures. IEEE signal processing magazine, vol.26, no.1, 98-117.

상세보기
Inamdar, Deep, Leblanc, George, Soffer, Raymond, Kalacska, Margaret. The Correlation Coefficient as a Simple Tool for the Localization of Errors in Spectroscopic Imaging Data. Remote sensing, vol.10, no.2, 231-.

상세보기

LOADING...

원문 보기

AccessON 원문보기

원문 URL 링크

DOI : 10.3390/atmos10050244 [무료]
Molecular Diversity Preservation International : 저널
AccessON : 저널

*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

내보내기 메뉴

내보내기 구분

파일저장
인쇄
메일전송

구성항목

기본정보
상세정보

관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관

저장형식

Text(ASCII format)
Excel format
RefWorks Direct Export
RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley

메일정보

받는사람 (필수): @
보내는사람 (선택): @
제목
내용: KISTI 검색결과 이메일 서비스

안내

총 건의 자료가 검색되었습니다.

다운받으실 자료의 인덱스를 입력하세요. (1-10,000)

검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다.

데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요)

다운로드 파일은 UTF-8 형태로 저장됩니다.
파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오.

Text(ASCII format)
Excel format

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

AI-Helper ※ AI-Helper는 을 사용합니다.

AI-Helper

안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

연합인증