[논문]A Method to Suppress False Alarms of Sentinel-1 to Improve Ship Detection

Bae, Jeongju; Yang, Chan-Su

doi:10.7780/kjrs.2020.36.4.4

A Method to Suppress False Alarms of Sentinel-1 to Improve Ship Detection 원문보기

대한원격탐사학회지 = Korean journal of remote sensing, v.36 no.4, 2020년, pp.535 - 544

Bae, Jeongju (Maritime Security and Safety Research Center, Korea Institute of Ocean Science and Technology) , Yang, Chan-Su (Maritime Security and Safety Research Center, Korea Institute of Ocean Science and Technology)

Abstract ▼ AI-Helper

In synthetic aperture radar (SAR) based ship detection application, false alarms frequently occur due to various noises caused by the radar imaging process. Among them, radio frequency interference (RFI) and azimuth smearing produce substantial false alarms; the latter also yields longer length estimation of ships than the true length. These two noises are prominent at cross-polarization and relatively weak at co-polarization. However, in general, the cross-polarization data are suitable for ship detection, because the radar backscatter from background sea surface is much less in comparison with the co-polarization backscatter, i.e., higher ship-sea image contrast. In order to improve the ship detection accuracy further, the RFI and azimuth smearing need to be mitigated. In the present letter, Sentinel-1 VV- and VH-polarization intensity data are used to show a novel technique of removing these noises. In this method, median image intensities of noises and background sea surface are calculated to yield arithmetic tendency. A band-math formula is then designed to replace the intensities of noise pixels in VH-polarization with adjusted VV-polarization intensity pixels that are less affected by the noises. To verify the proposed method, the adaptive threshold method (ATM) with a sliding window was used for ship detection, and the results showed that the 74.39% of RFI false alarms are removed and 92.27% false alarms of azimuth smearing are removed.

주제어

표/그림 (9)

그림 Fig. 1. Area of interest (AOI) in this study.
그림 Fig. 2. (Top) Sentinel-1B false-color image acquired on Sep. 24, 2018.
표 Table 1. Sentinel-1 IW GRD dataset used in this study
그림 Fig. 3. (Left) The relationship between the incidence angle and intensity of background sea in the scene number 18 of Table 2. (Right) The relationship between the wind speed from Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) generated in the Korea Operational Oceanographic System (KOOS) (Park et al., 2015) and median intensity of background sea in the scene number 15 in Table 2.
표 Table 2. Median intensity of ocean, RFI and azimuth smearing (unit: decibel representation of normalized radar cross section)
그림 Fig. 4. Overall ship detection flow.
그림 Fig. 5. Pseudocolor images before and after the application of the proposed method for bottom part of Fig. 2.
표 Table 3. Number of false alarms in ship detection result
그림 Fig. 6. Comparison of the median intensity of 100 ships in the VH polarization image and the proposed method.

AI 본문요약
AI-Helper

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문제 정의

Hence, the purpose of this study is to reduce false alarms caused by RFI and azimuth smearing for ship detection with ATM in Sentinel-1 GRD data. In particular, it will be shown that VH polarization is suitable for ship detection in comparison with VV polarization, although it is vulnerable to RFI and azimuth smearing i.
This research is a part of the projects entitled “Development of Satellite-based System on Monitoring and Predicting Ship Distribution in the Contiguous Zone”, “Construction of Ocean Research Stations and their application Studies”, “Technology Development for Practical Applications of Multi-Satellite Data to Maritime Issues”, funded by the Ministry of Oceans and Fisheries, Korea.

제안 방법

Thus, we introduce an effective method combining two polarizations to reduce false alarms from RFI and azimuth smearing. The key strength and novelty of this work are its simplicity and efficiency to reduce numerous false alarms using only a single pixel of intensity value for each polarization.

대상 데이터

In this work, we used the Sentinel-1 GRD products over the waters off Busan, South Korea. The period of the dataset acquisition is from Dec.

성능/효과

1) VH polarization is less affected by incidence angle and wind speed, and therefore it is suitable for ship detection in comparison with VV polarization when RFI and azimuth smearing do not exist.2) Only 10% of RFI and azimuth smearing pixels at VV polarization are 5.
1) VH polarization is less affected by incidence angle and wind speed, and therefore it is suitable for ship detection in comparison with VV polarization when RFI and azimuth smearing do not exist.2) Only 10% of RFI and azimuth smearing pixels at VV polarization are 5.93 dB and 6.53 dB higher than VH polarization on average, respectively. Therefore, more than 90% of the noise pixels meet the following condition.
Without equation (2), ship detection using VH polarization resulted in 9,059 false alarms from the RFI and 220 false alarms from the azimuth smearing, respectively, as in Table 3.After applying the proposed method, the ship detection result showed 2,320 false alarms (74.39% decrease) in RFI and 17 false alarms (92.27% decrease) in azimuth smearing, respectively.
To reduce false alarms from RFI and azimuth smearing in the VH polarization, we designed a band-math formula using VV polarization data. Test on the 20 Sentinel-1 images showed a 74.39% reduction in RFI caused false alarms, and 92.27% reduction in azimuth smearing caused false alarms. The proposed method has the side effect of decreasing the average intensity by 0.

참고문헌 (23)

Brusch, S., S. Lehner, T. Fritz, M. Soccorsi, A. Soloviev, and B. V. Schie, 2011. Ship surveillance with TerraSAR-X, IEEE Transactions on Geoscience and Remote Sensing, 49(3): 1092-1103.

상세보기
Chan, Y. K. and V. C. Koo, 2008. An introduction to synthetic aperture radar (SAR), Progress In Electromagnetics Research B, 2: 27-60.
Crisp, D. J., 2004. The state-of-art in ship detection in synthetic aperture radar imagery, Defense Science and Technology Organization, Fairbairn, Canberra, Australia.
Fienup, J. R. and A. M. Kowalczyk, 1995. Detecting moving targets in SAR imagery by using a phase-error correction algorithm, Proc. of Algorithms for Synthetic Aperture Radar Imagery II, Bellingham, WA, Jun. 5, vol. 2487, pp. 337-344.
Greidanus, H., P. Clayton, M. Indregard, G. Staples, N. Suzuki, P. Vachoir, C. Wackerman, T. Tennvassas, J. Mallorqui, N. Kourti, R. Ringrose, and H. Melief, 2004. Benchmarking operational SAR ship detection, Proc. of IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, Sep. 20-24, vol. 6, pp. 4215-4218.
Greidanus, H. and N. Kourti, 2006. Findings of the DECLIMS project-detection and classification of marine traffic from space, Proc. of SEASAR: Advances in SAR Oceanography from Envisat and ERS Missions, Frascati, Italy, Jan. 23-26.
Hannevik, T. N., K. Eldhuset, and R. Olsen, 2015. Improving ship detection by using polarimetric decompositions, Norwegian Defense Research Establishment, Kjeller, Norway.
Hong, D. B., J. J. Bae, and C. S. Yang, 2018. Automatic mapping technique of sea ice in the coastal waters of the Arctic Ocean using Sentinel-1 data, Journal of Coastal Research, 85: 556-560.

상세보기
Lee, J., 1980. Digital image enhancement and noise filtering by use of local statistics, IEEE Transactions on Pattern Analysis & Machine Intelligence, PAMI-2(2): 165-168.
Liu, C., 2015. A dual-polarization ship detection algorithm, Defense Research and Development, Ottawa, Canada.
Meadows, P. J., 2019. S-1A & S-1B annual performance report for 2018, European Space Agency, Paris, France.
Meyer, F. J., J. B. Nicoll, and A. P. Doulgeris, 2013. Correction and characterization of radio frequency interference signatures in l-band synthetic aperture radar data, IEEE Transactions on Geoscience and Remote Sensing, 51(10): 4961-4972.

상세보기
Miranda, N. and P. J. Meadows, 2015. Radiometric calibration of S-1 Level-1 products generated by the S-1 IPF, European Space Agency, Paris, France.
Ouchi, K., 2004. Principles of synthetic Aperture Radar for remote sensing, Tokyo Denki University, Tokyo, Japan.
Park, K.-S., K.-Y. Heo, K. Jun, J.-I. Kwon, J. Kim, J.-Y. Choi, K.-H. Cho, B.-J. Choi, S.-N. Seo, Y.-H. Kim, S.-D. Kim, C.-S. Yang, J.-C. Lee, S.-I. Kim, S. Kim, J.-W. Choi, and S.-H. Jeong, 2015. Development of the operational oceanographic system of Korea, Ocean Science Journal, 50(20): 353-369.
Pelich, R., M. Chini, R. Hostache, P. Matgen, C. Lopez- Martinez, M. Nuevo, P. Ries, and G. Eiden, 2019. Large-scale automatic vessel monitoring based on dual-polarization Sentinel-1 and AIS data, Remote Sensing, 11(9): 1078.
Santamaria, C., M. Alvarez, H. Greidanus, V. Syrris, P. Soille, and P. Argentieri, 2017. Mass processing of Sentinel-1 images for maritime surveillance, Remote Sensing, 9(7): 678-697.

상세보기
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008. A description of the Advanced Research WRF version 3, National Center for Atmospheric Research, Boulder, CO, USA.
Small, D. and A. Schubert, 2008. Guide to ASAR geocoding, European Space Agency, Paris, France.
Tao, M., J. Su, Y. Huang, and L. Wang, 2019. Mitigation of radio frequency interference in synthetic aperture radar data: current status and future trends, Remote Sensing, 11(20): 2438.
Vachon, P. W. and J. Wolfe, 2010. C-band crosspolarization wind speed retrieval, IEEE Geoscience and Remote Sensing Letters, 8(3): 456-459.
Wang, Y., C. Wang, and H. Zhang, 2018. Ship discrimination with deep convolutional neural networks in SAR Images, Proc. of 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Jul. 22-27, pp. 8444-8447.
Yang, C. S., J. H. Park, and H.-A. Rashid, 2018. An improved method of land masking for synthetic aperture radar-based ship detection, The Journal of Navigation, 71(4): 788-804.

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표제어: PCR

동의어: Packet Collision Rate

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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].

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