Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 2, pp. 18-28
Standard level processing of Resurs-P KShMSA data for automatic generation of seamless continuous coverage
A.I. Vasiliev
1 , A.V. Krylov
1 , A.V. Pankin
1 1 Research Center for Earth Operative Monitoring of Russian Space Systems JSC, Moscow, Russia
Accepted: 19.03.2019
DOI: 10.21046/2070-7401-2019-16-2-18-28
The paper deals with the main algorithms for standard level processing of data from the wide-swath multispectral equipment (KShMSA) on the Resurs-P spacecraft. The algorithms are designed for automatic generation of a seamless continuous coverage (BSP). First, the geometric model of imaging system and its calibration results are given. Second, the relative radiometric correction technique is proposed to monitor the radiometric homogeneity across the entire image area. Third, the specific features of the KShMSA data photogrammetric processing up to the 1D CEOS Level including the geolocation control and adjustment are considered. The technology of automated generation of seamless continuous coverage based on the KShMSA data using the photogrammetric software packages is given with the need for cloudiness masking in automatic generation of BSP being pointed out. The algorithm of cloud pixel classification for the KShMSA data is proposed. Taking the Resurs-P No. 1 and No. 2 ShMSA-VR data (15 strips) on the Samara Region during summer seasons 2015 and 2016 as an example, the Level 0 to Level 1D CEOS processing was done fully automatically including cloud masking, and the BSP was generated automatically (using the Photomod software).
Keywords: Earth remote sensing, Resurs-P spacecraft, wide-swath multispectral equipment, standard level processing, seamless continuous coverage, mosaic
Full textReferences:
- Baklanov A. I., Afonin A. N., Blinov V. D., Zabiyakin A. S., KShMSA ― kompleks shirokozakhvatnoi mul’tispektral’noi apparatury kosmicheskogo apparata “Resurs-P” (KShMSA ― The system of wide swath multispectral apparatus “Resurs-P”), Vestnik Samarskogo gosudarstvennogo aerokosmicheskogo universiteta imeni akademika S. P. Koroleva, 2016, Vol. 15, No. 2, pp. 22–29.
- Blinov V. D., Kvitka V. E., Kompleks KSHMSA. Opisanie algoritmov nazemnoi obrabotki informatsii KSHMSA v izdelii 47KS. Chast’ 3. Algoritm № 3 KSH. CTEA 1.701.074 D 4.2 (The complex of wide-swath multispectral equipment. Description of on-ground CSWE information processing algorithms of in the 47KC unit), NPP “OPTJeKS”, 2012, 12 p.
- Bocharnikov A. I., Zhilichkin A. G., Kovalenko V. P., Kondratov A. V., Tikhonychev V. V., Khudiakov A. V., Tekhnologii opredeleniya kharakteristik tselevoi apparatury KK DZZ (Techniques for characterization of remote sensing spacecraft targeted equipment), Raketno-kosmicheskoe priborostroenie i informatsionnye sistemy, 2015, Vol. 2, Issue 2, pp. 18–31.
- Vasilyev A. I., Olshevskii N. A., Korshunov A. P., Bank bazovykh produktov mezhvedomstvennogo ispol’zovaniya ― geoinformatsionnyi servis operatora KS DZZ (Basic product bank of interagency use ― geinformation service of remote sensing space systems operator), 14-ya Vserossiiskaya otkrytaya konfentsiya “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (14th All-Russia Open Conf. “Current Problems of Remote Sensing of the Earth from Space”), Book of Abstracts, Moscow, 2016, p. 419, available at: http://smiswww.iki.rssi.ru/d33_conf/thesisshow.aspx?page=133&thesis=5633.
- Vasilyev A. I., Stremov A. S., Kovalenko V. P. (2017a), Issledovanie dannykh kompleksa shirokozakhvatnoi mul’tispektral’noi apparatury KA “Resurs-P” dlya resheniya spektrometricheskikh zadach (Study of Resurs-P wide-swath multispectral equipment data applicability to spectrometric tasks), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 4, pp. 36–51.
- Vasilyev A. I., Stremov A. S., Mikheev A. A. (2017b), Issledovanie dinamiki izmeneniya parametrov absolyutnoi kalibrovki KShMSA KA “Resurs-P” (The study of dynamics change for absolute calibration parameters of KShMSA Resurs-P), 15-ya Vserossiiskaya otkrytaya konfentsiya “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (15th All-Russia Open Conf. “Current Problems of Remote Sensing of the Earth from Space”), Book of Abstracts, Moscow, 2017, p. 448, available at: http://smiswww.iki.rssi.ru/d33_conf/thesisshow.aspx?page=144&thesis=6202.
- Loupian E. A., Savorskii V. P., Bazovye produkty obrabotki dannykh distantsionnogo zondirovaniya Zemli (Basic remote sensing data products), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 2, pp. 87–97.
- Markov A. N., Vasilyev A. I., Olshevsky N. A., Korshunov A. P., Mikhalenkov R. A., Salimonov B. B., Stremov A. S., Arkhitektura geoinformatsionnogo servisa “Bank bazovykh produktov” (Architecture of the Basic Product Bank geoinformation service), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 5, pp. 39–51.
- Fischler M. A., Bolles R. C., Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Communications of the ACM, 1981, Vol. 24, Issue 6, pp. 381–395.
- Grodecki J., Dial G., IKONOS Geometric Accuracy, Proc. Joint Workshop of ISPRS Working Groups I/2, I/5 and IV/7 on High Resolution Mapping from Space 2001, University of Hannover, 2001, pp. 77–86.
- Hollingsworth B., Chen L., Reichenbach S. E., Irish R. R., Automated cloud cover assessment for Landsat TM images, Proc. SPIE, Vol. 2819: Imaging Spectrometry II, 1996, pp. 170–179.
- Kang Y., Pan L., Zhang T., Zhang S., Liu X., Automatic mosaicking of satellite imagery considering the clouds, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 23rd ISPRS Congress, 2016, Vol. III-3, pp. 415–421.
- Leslie C. R., Serbina L. O., Miller H. M., Landsat and agriculture — Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production: Open-File Report 2017–1034, U. S. Geological Survey, Reston, Virginia: 2017, 34 p., available at: https://pubs.er.usgs.gov/publication/ofr20171034 (March 12, 2019).
- Lowe D. G., Distinctive image features from scale-invariant keypoints, Intern. J. Computer Vision, 2004, Vol. 60, No. 2, pp. 91–110.
- Poli D., A Rigorous Model for Spaceborne Linear Array Sensors, Photogrammetric Engineering & Remote Sensing, 2007, Vol. 73, No. 2, pp. 187–196.
- Ramoino F., Tutunaru F., Pera F., Arino O., Ten-Meter Sentinel-2A Cloud-Free Composite — Southern Africa 2016, Remote Sensing, 2017, Vol. 9, Issue 7, p. 652, available at: https://www.mdpi.com/2072-4292/9/7/652.
- Vasilyev A. I., Boguslavskiy A. A., Sokolov S. M., Parallel SIFT-detector implementation for images matching, Proc. 21st Conf. Computer Graphics and Vision (GraphiCon’2011), Moscow, 2011, pp. 173–176.
- Zhu Z., Woodcock C. E., Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment, 2012, Vol. 118, pp. 83–94.