Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 4, pp. 88-99
Cloud data automatic filtering algorithm for object remote monitoring
1 Space Research Institute RAS, Moscow, Russia
Accepted: 25.08.2022
DOI: 10.21046/2070-7401-2022-19-4-88-99
The paper describes an approach to automatic filtering of cloud data to be used by the object monitoring technology developed at the Space Research Institute (IKI). The technology was created to monitor and control the dynamics of various natural and anthropogenic objects (homogeneous formations). It provides automatic creation and analysis of long-term series of various characteristics of observed objects obtained from satellite data. One of the problems in the creation of such series is the organization of automatic filtering of incorrect data, which is associated, first of all, with the presence of clouds covering the object in the satellite images. The organization of such filtering can be difficult in cases when it is not possible to provide a reliable automatic selection of clouds. The paper presents an algorithm that allows automatic filtering in such a situation. It is based on an integral analysis of cloudiness and the search of percentage thresholds of cloudiness coverage in the area of the observed object instead of cloudiness analysis of individual pixels. The values of object characteristics calculated from data with cloudiness coverage percentage higher than the defined threshold would almost certainly be false. The paper provides an analysis and presents the possibility of optimal thresholds definition which allow to filter out a maximum number of false data along with loosing a minimum number of cloudless data. The paper also gives examples of using the proposed filtering algorithm to derive series of characteristics of objects located in regions with different observation conditions. Also, the results of the proposed filtering method are compared with the results of other filtering methods, in particular, noise filtering in a series of object characteristics.
Keywords: remote sensing, natural objects, technology, object monitoring, cloud filtering, noise filtering, IKI-Monitoring Center for Collective Use
Full textReferences:
- Bartalev S., Egorov V., Zharko V., Loupian E., Plotnikov D., Khvostikov S., Shabanov N., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data), Moscow: IKI RAN, 2016, 208 p. (in Russian).
- Konstantinova A. M., Balashov I. V., Kashnitskiy A. V., Loupian E. A., Unified technology for remote monitoring of natural and anthropogenic objects, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 4, pp. 41–52 (in Russian), DOI: 10.21046/2070-7401-2021-18-4-41-52.
- Loupian E. A., Proshin A. A., Bourtsev M. A., Kashnitskiy A. V., Balashov I. V., Bartalev S. A., Konstantinova A. M., Kobets D. A., Mazurov A. A., Marchenkov V. V., Matveev A. M., Radchenko M. V., Sychugov I. G., Tolpin V. A., Uvarov I. A., Experience of development and operation of the IKI-Monitoring center for collective use of systems for archiving, processing and analyzing satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 3, pp. 151–170 (in Russian), DOI: 10.21046/2070-7401-2019-16-3-151-170.
- Loupian E. A., Konstantinova A. M., Balashov I. V., Kashnitskiy A. V., Savorsky V. P., Panova O. Yu., Development of a system for analyzing the state of environment in areas of large industrial facilities, tailings and dumps, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 7, pp. 243–261 (in Russian), DOI: 10.21046/2070-7401-2020-17-7-243-261.
- Merkov A. B., Raspoznavanie obrazov. Vvedenie v metody statisticheskogo obucheniya (Automatic cognition. Introduction to statistical learning methods), 2011, 256 p. (in Russian).
- Muhamedzhanov I. D., Konstantinova A. M., Loupian E. A., Umirzakov G. U., Evaluation of satellite monitoring capabilities of stream runoff based on the Amu Darya River state analysis, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 1, pp. 87–103 (in Russian), DOI: 10.21046/2070-7401-2022-19-1-87-103.
- Baetens L., Desjardins C., Hagolle O., Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure, Remote Sensing, 2019, Vol. 11, Art. No. 433, DOI: 10.3390/rs11040433.
- Coluzzi R., Imbrenda V., Lanfredi M., Simoniello T., A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses, Remote Sensing of Environment, 2018, Vol. 217, pp. 426–443, DOI: 10.1016/j.rse.2018.08.009.
- Copernicus Sentinel-2 pre-Collection-1 MSI Level-1C (L1C) (2021a), European Space Agency, 2021, https://doi.org/10.5270/S2_-d8we2fl.
- Copernicus Sentinel-2 pre-Collection-1 MSI Level-2A (L2A) (2021b), European Space Agency, 2021, https://doi.org/10.5270/S2_-6eb6imz.
- Dozier J., Spectral signature of alpine snow cover from the Landsat Thematic Mapper, Remote Sensing of Environment, 1989, Vol. 28, pp. 9–22, DOI: 10.1016/0034-4257(89)90101-6.
- Luo Y., Guan K., Peng J., Wang S., Huang Y., STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product, Remote Sensing, 2020, Vol. 12(19), Art. No. 3209, 21 p., DOI: 10.3390/rs12193209.
- Zhu Z., Wang S., Woodcock C., Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images, Remote Sensing of Environment, 2015, Vol. 159, pp. 269–277, DOI: 10.1016/j.rse.2014.12.014.