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An Effective Satellite Remote Sensing Tool Combining Hardware and Software Solutions

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Sensor Networks (SENSORNETS 2018, SENSORNETS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1074))

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Abstract

In this paper we propose a new effective remote sensing tool combining hardware and software solutions as an extension of our previous work. In greater detail the tool consists of a low cost receiver subsystem for public weather satellites and a signal and image processing module for several tasks such as signal and image enhancement, image reconstruction and cloud detection. Our solution allows to manage data from satellites effectively with low cost components and portable software solutions. We aim at sampling and processing of the modulated signal entirely in software enabled by Software Defined Radios (SDR) and CPU computational speed overcoming hardware limitation such as high receiver noise and low ADC resolution. Since we want to extend our previous method to demodulate signals coming from various meteorological satellites, we propose a new high frequency receiving system designed to receive and demodulate signals transmitted at 1.7 GHz. The signals coming from satellites are demodulated, synchronized and enhanced by using low level image processing techniques, then cloud detection is performed by using the well known K-means clustering algorithm. The hardware and software architecture extensions make our solution able to receive and demodulate high frequency and bandwidth meteorological satellite signals, such as those transmitted by NOAA POES, NOAA GOES, EUMETSAT Metop, Meteor-M and FengYun.

Francesco Gugliuzza and Alessandro Bruno contributed equally to this work.

A. Bruno—The contribution of Alessandro Bruno falls within the activities of the current project titled “I telescopi Cherenkov per lo sviluppo tecnologico e culturale della Sicilia” at INAF-IASF Palermo, under the scientific supervision of Researcher Dr. Anna Anzalone.

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References

  1. Ardizzone, E., Bruno, A., Gugliuzza, F., Pirrone, R.: A low cost solution for NOAA remote sensing. In: Proceedings of the 7th International Conference on Sensor Networks (SENSORNETS 2018), SCITEPRESS - Science and Technology Publications, Lda. All rights reserved, pp. 128–134 (2018)

    Google Scholar 

  2. MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281–297 (1967)

    Google Scholar 

  3. Farserotu, J., Prasad, R.: A survey of future broadband multimedia satellite systems, issues and trends. IEEE Commun. Mag. 38(6), 128–133 (2000)

    Article  Google Scholar 

  4. Rahmat-Samii, Y., Densmore, A.C.: Technology trends and challenges of antennas for satellite communication systems. IEEE Trans. Antennas Propag. 63(4), 1191–1204 (2015)

    Article  MathSciNet  Google Scholar 

  5. Al-Moustafa, T., Armitage, R.P., Danson, F.M.: Mapping fuel moisture content in upland vegetation using airborne hyperspectral imagery. Remote Sens. Environ. 127, 74–83 (2012)

    Article  Google Scholar 

  6. Camps-Valls, G., Benediktsson, J.A., Bruzzone, L., Chanussot, J.: Introduction to the issue on advances in remote sensing image processing. IEEE J. Sel. Top. Signal Process. 5(3), 365–369 (2011)

    Article  Google Scholar 

  7. Barnes, J.C., Smallwood, M.D.: TIROS-N series direct readout services users guide. National Earth Satellite Service, NOAA (1982)

    Google Scholar 

  8. Wallach, J.: User’s Guide for Building and Operating Environmental Satellite Receiving Stations. National Environmental Satellite, Data, and Information Service, NOAA (1997)

    Google Scholar 

  9. Benabadji, N., Hassini, A., Belbachir, A.H.: Hardware and software consideration to use NOAA images. Revue Internationale des Energies Renouvelables, CDER 7(01), 1–11 (2004)

    Google Scholar 

  10. Moradi, I., Meng, H., Ferraro, R.R., Bilanow, S.: Correcting geolocation errors for microwave instruments aboard NOAA satellites. IEEE Trans. Geosci. Remote Sens. 51(6), 3625–3637 (2013)

    Article  Google Scholar 

  11. Bosquez, C., Ramos, A., Noboa, L.: System for receiving NOAA meteorological satellite images using software defined radio. In: ANDESCON, 2016 IEEE, pp. 1–4. IEEE (2016)

    Google Scholar 

  12. Mahmood, S., Mushtaq, M.T., Jaffer, G.: Cost efficient design approach for receiving the NOAA weather satellites data. In: 2016 IEEE Aerospace Conference, pp. 1–6. IEEE (2016)

    Google Scholar 

  13. Uengtrakul, B., Bunnjaweht, D.: A cost efficient software defined radio receiver for demonstrating concepts in communication and signal processing using Python and RTL-SDR. In: 2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), pp. 394–399. IEEE (2014)

    Google Scholar 

  14. Sruthi, M., Abirami, M., Manikkoth, A., Gandhiraj, R., Soman, K.: Low cost digital transceiver design for Software Defined Radio using RTL-SDR. In: 2013 International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), pp. 852–855. IEEE (2013)

    Google Scholar 

  15. Feidas, H., Cartalis, C., Cracknell, A.: Use of Meteosat imagery to define clouds linked with floods in Greece. Int. J. Remote Sens. 21(5), 1047–1072 (2000)

    Article  Google Scholar 

  16. Heidinger, A.K., Evan, A.T., Foster, M.J., Walther, A.: A naive Bayesian cloud-detection scheme derived from CALIPSO and applied within PATMOS-x. J. Appl. Meteorol. Climatol. 51(6), 1129–1144 (2012)

    Article  Google Scholar 

  17. Alkhatib, M.Q., Cabrera, S.D., Gill, T.E.: Automated detection of dust clouds and sources in NOAA-AVHRR satellite imagery. In: 2012 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 97–100. IEEE (2012)

    Google Scholar 

  18. An, Z., Shi, Z.: Scene learning for cloud detection on remote-sensing images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(8), 4206–4222 (2015)

    Article  Google Scholar 

  19. Ackerman, S., et al.: Discriminating clear-sky from cloud with MODIS algorithm theoretical basis document (MOD35). ATBD Ref. ATBD-MOD-06, version 4 (1997) 115p MODIS Cloud Mask Team. University of Wisconsin, Cooperative Institute for Meteorological Satellite Studies (1997)

    Google Scholar 

  20. Lin, C.H., Lin, B.Y., Lee, K.Y., Chen, Y.C.: Radiometric normalization and cloud detection of optical satellite images using invariant pixels. ISPRS J. Photogrammetry Remote Sens. 106, 107–117 (2015)

    Article  Google Scholar 

  21. Karlsson, K.G., Johansson, E., Devasthale, A.: Advancing the uncertainty characterisation of cloud masking in passive satellite imagery: probabilistic formulations for NOAA AVHRR data. Remote Sens. Environ. 158, 126–139 (2015)

    Article  Google Scholar 

  22. Simpson, J.J., Gobat, J.I.: Improved cloud detection for daytime AVHRR scenes over land. Remote Sens. Environ. 55(1), 21–49 (1996)

    Article  Google Scholar 

  23. González, A., Pérez, J.C., Muñoz, J., Méndez, Z., Armas, M.: Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery. Adv. Space Res. 49(1), 135–142 (2012)

    Article  Google Scholar 

  24. Yuan, Y., Hu, X.: Bag-of-words and object-based classification for cloud extraction from satellite imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(8), 4197–4205 (2015)

    Article  Google Scholar 

  25. Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B.: Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017)

    Article  Google Scholar 

  26. Bai, T., Li, D., Sun, K., Chen, Y., Li, W.: Cloud detection for high-resolution satellite imagery using machine learning and multi-feature fusion. Remote Sens. 8(9), 715 (2016)

    Article  Google Scholar 

  27. Ardizzone, E., Bruno, A., Mazzola, G.: Scale detection via keypoint density maps in regular or near-regular textures. Pattern Recogn. Lett. 34(16), 2071–2078 (2013)

    Article  Google Scholar 

  28. Yhann, S.R., Simpson, J.J.: Application of neural networks to AVHRR cloud segmentation. IEEE Trans. Geosci. Remote Sens. 33(3), 590–604 (1995)

    Article  Google Scholar 

  29. Shi, M., Xie, F., Zi, Y., Yin, J.: Cloud detection of remote sensing images by deep learning. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 701–704. IEEE (2016)

    Google Scholar 

  30. Griffiths, M.: Turnstile design — DigitalHam (2014). http://www.digitalham.co.uk/design

  31. Osmocom: rtl-sdr - OsmoSDR (2012). http://sdr.osmocom.org/trac/wiki/rtl-sdr

  32. Sergienko, A.B.: Software-defined radio in MATLAB simulink with RTL-SDR hardware. In: 2014 International Conference on Computer Technologies in Physical and Engineering Applications (ICCTPEA), pp. 160–161. IEEE (2014)

    Google Scholar 

  33. Keen, K.: Rtl\(\_\)fm Guide: Updates for rtl\(\_\)fm overhaul (2013). http://kmkeen.com/rtl-demod-guide

  34. Magliacane, J.: PREDICT - A Satellite Tracking/Orbital Prediction Program (2001). http://www.qsl.net/kd2bd/predict.html

  35. Lyons, R.G.: Understanding Digital Signal Processing, 3/E. Pearson Education India (2004)

    Google Scholar 

  36. Shima, J.M.: FM demodulation using a digital radio and digital signal processing. Master’s thesis, University of Florida (1995)

    Google Scholar 

  37. Wilson, J., Nelson, A., Farhang-Boroujeny, B.: Parameter derivation of type-2 discrete-time phase-locked loops containing feedback delays. IEEE Trans. Circuits Syst. II Express Briefs 56(12), 886–890 (2009)

    Article  Google Scholar 

  38. Leconte, T.: ATPDEC Home Page (2003). http://atpdec.sourceforge.net

  39. The GNU Radio Foundation: GNU Radio (2001). https://www.gnuradio.org

  40. The GNU Radio Foundation: Vector-Optimized Library of Kernels (2015). http://libvolk.org

  41. The GNU Radio Foundation: NOAA POES HRPT receiver (2009) https://github.com/gnuradio/gnuradio/tree/master/gr-noaa

  42. Csete, A.: NOAA Weather Satellite Reception with GNU Radio and USRP (2010). http://oz9aec.net/radios/gnu-radio/noaa-weather-satellite-reception-with-gnu-radio-and-usrp

  43. Bülo, M.: A Simple GnuRadio HRPT Decoder (2018). https://tynet.eu/hrpt-decoder

  44. Zhang, J., Hu, J.: Image segmentation based on 2D Otsu method with histogram analysis. In: 2008 International Conference on Computer Science and Software Engineering, vol. 6, pp. 105–108. IEEE (2008)

    Google Scholar 

  45. Dartcom Systems Ltd.: HRPT/AHRPT System (2018). https://www.dartcom.co.uk/products/hrpt-ahrpt-system

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Author information

Authors and Affiliations

  1. Dipartimento dell’Innovazione Industriale e Digitale (DIID), Università degli Studi di Palermo, Viale delle Scienze Ed. 6, 90128, Palermo (PA), Italy

    Francesco Gugliuzza, Alessandro Bruno, Edoardo Ardizzone & Roberto Pirrone

  2. INAF-IASF Palermo Istituto Nazionale di Astrofisica, Istituto di Astrofisica Spaziale e Fisica Cosmica, Via Ugo La Malfa 153, 90146, Palermo (PA), Italy

    Alessandro Bruno

Authors
  1. Francesco Gugliuzza
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  2. Alessandro Bruno
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  3. Edoardo Ardizzone
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  4. Roberto Pirrone
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Corresponding author

Correspondence to Francesco Gugliuzza .

Editor information

Editors and Affiliations

  1. ETS Ingenieía y Sistemas de Teleco, Universidad Politécnica de Madrid, Madrid, Spain

    César Benavente-Peces

  2. Cisco Systems, San Jose, CA, USA

    Nancy Cam-Winget

  3. École Normale Supérieure Lettres et Sciences Humaines, Lyon, France

    Eric Fleury

  4. Fakultät für Ingenieurwissenschaften, Hochschule Wismar, Wismar, Mecklenburg-Vorpommern, Germany

    Andreas Ahrens

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Gugliuzza, F., Bruno, A., Ardizzone, E., Pirrone, R. (2019). An Effective Satellite Remote Sensing Tool Combining Hardware and Software Solutions. In: Benavente-Peces, C., Cam-Winget, N., Fleury, E., Ahrens, A. (eds) Sensor Networks. SENSORNETS SENSORNETS 2018 2017. Communications in Computer and Information Science, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-30110-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-30110-1_9

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-30110-1

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