دسته بندی تصاویر با تفکیک بسیار بالای پرتوی با استفاده از محاسبات نرم

نویسنده

دانشگاه شهید بهشتی

چکیده

هدف اصلی این مقاله به کارگیری روشی بر مبنای الگوریتم‌های هوش محاسباتی برای تهیه نقشه پرتوی است که به دسته بندی الگوهای مختلف شناسایی مناطق آلوده پرتوی و تغییر آلودگی پرتوی می‌پردازد. در این مقاله، برای تعیین درجه آلودگی پرتوی مناطق، استفاده از سیستم استنتاج فازی پیشنهاد شده است. در این تحقیق از داده های از طیف سنج پرتوی با قدرت تفکیک بسیار بالا (Ultra-high Resolution Spectrometry) در زمینه کشف اورانیوم استفاده شده است. حوزه پژوهش شامل ذخایر اورانیوم شناخته شده از جمله لامباپور-پداگاتو، چیتریال و کپونرو است. داده‌های طیف سنجی پرتوی با رزولوشن بسیار بالا که برای اکتشاف اورانیوم جمع آوری شده بود برای تخمین میانگین نرخ جذب در هوا به سبب توزیع پرتو ماده‌ها (پتاسیم در درصد و اورانیوم و توریم در بخش در میلیون) در این مناطق مورد استفاده واقع شد. همچنین برای تعیین میزان آلودگی پرتوی هر منطقه از سیستم استنتاج فازی ممدانی استفاده شده است. نتایج بدست آمده کارایی این روش را با دقتی برابر با 76 درصد برای آشکارسازی سه سطح آلودگی پرتوی (بدون آلودگی پرتوی یا آلودگی پرتوی کم، آلودگی پرتوی متوسط و آلودگی پرتوی زیاد) و 89 درصد برای شناسایی کلی مناطق آلوده از مناطق فاقد آلودگی پرتوی برآورد نمود.

کلیدواژه‌ها


عنوان مقاله [English]

Classification of Ultra-high Resolution Images using Soft Computing

نویسنده [English]

  • Mostafa Borhani
چکیده [English]

The main objective of this paper is to use a computational intelligence algorithm for preparing a mapping map that categorizes different patterns of identification of infected areas and changes in radiation pollution. In this paper, the use of the fuzzy inference system has been proposed to determine the degree of radiation contamination in the regions. The study uses ultra-high resolution spectrometry data to detect uranium. The research area includes well-known uranium deposits, including LambaPur-Peddagattu, Chitrial and Koppunuru. The high-resolution Spectrometry data collected for uranium exploration was used to estimate the average absorption rate in the air due to the distribution of females (potassium per cent and uranium and thorium per million) in these areas. Mamdani's Fuzzy Inference System has also been used to determine the amount of radiation contamination in each region. The results showed that the efficiency of this method was 76% accurate for the detection of three levels of radiation contamination (no radiation contamination, low radiation exposure, medium radiation and high radiation pollution) and 89% for the overall identification of contaminated areas from non-polluted areas.

کلیدواژه‌ها [English]

  • Computational intelligence
  • Ultra-high Resolution Spectrometry
  • Fuzzy Inference of Mamadani
  • Radiation Map
  • Fuzzy Inference
  • Genetic algorithm
  • SVM
[1] D. C. W. Sanderson, A. J. Cresswell & D. C. White (2008) The effect of flight line spacing on radioactivity inventory and spatial feature characteristics of airborne gamma‐ray spectrometry data, International Journal of Remote Sensing, 29:1, 31-46, : 10.1080/01431160701268970 [2] D. Srinivas, V. Ramesh Babu, I. Patra, Shailesh Tripathi, M.S. Ramayya, A.K. Chaturvedi, Assessment of background gamma radiation levels using airborne gamma ray spectrometer data over uranium deposits, Cuddapah Basin, India – A comparative study of dose rates estimated by AGRS and PGRS, Journal of Environmental Radioactivity, Volume 167, 2017, Pages 1-12. [3] Mohsen Rezaei, Mansour Ashoor, Leila Sarkhosh, Numerical evaluation of gamma radiation monitoring, Nuclear Engineering and Technology, Volume 51, Issue 3, 2019, Pages 807-817, ISSN 1738-5733, https://doi.org/10.1016/j.net.2018.12.020. [4] Sanderson, D. , Allyson, J.D., Tyler, A.N., Ni Riain, S. and Murphy, S. (1993) An Airborne Gamma Ray Survey of Parts of SW Scotland in February 1993. Final Report. Project Report. Scottish Universities Research and Reactor Centre, Glasgow, UK. [5] Shengqing Xiong, Nanping Wang, Zhengguo Fan, Xingming Chu, Qifan Wu, Shaoying Pei, Jianhua Wan & Lihui Zeng (2012) Mapping the terrestrial air-absorbed gamma dose rate based on the data of airborne gamma-ray spectrometry in southern cities of China, Journal of Nuclear Science and Technology, 49:1, 61-70, DOI: 10.1080/18811248.2011.636550 [6] Martin, P., Tims, S., McGill, A. et al., “Use of Airborne γ-Ray Spectrometry for Environmental Assessment of the Rehabilitated Nabarlek Uranium Mine, Australia,” Environmental Monitoring and Assessment, Volume 115, Issue 1–3, pp 531–554., 2006. [7] D. Beamish, “Environmental radioactivity in the UK: the airborne geophysical view of dose rate estimates,” J Environ Radioact. 2014 Dec; 138:249-63. doi: 10.1016/j.jenvrad.2014.08.025. [8] Katti, V.J. & Dattanarayana, T.A. & Sreehari, R. & Kak, S.N.. (1997). Radiation monitoring in the environs of nuclear power plants in india using airborne gamma-ray spectrometry. 10. 107-118. [9] M, Sowmya & Senthilkumar, Bojarajan & Seshan, Ranga & Govindasamy, Hariharan & Ramachandran, Purvaja & Ramkumar, S & Ramachandran, Ramesh. (2010). Natural radioactivity and associated dose rates in soil samples from Kalpakkam, South India. Radiation protection dosimetry. 141. 239-47. 10.1093/rpd/ncq169. [10] N. Karunakara, I. Yashodhara, K. Sudeep Kumara, R.M. Tripathi, S.N. Menon, S. Kadam, M.P. Chougaonkar, Assessment of ambient gamma dose rate around a prospective uranium mining area of South India – A comparative study of dose by direct methods and soil radioactivity measurements, Results in Physics, Volume 4, 2014, Pages 20-27, ISSN 2211-3797, https://doi.org/10.1016/j.rinp.2014.02.001. [11] UNSCEAR, “United Nations Scientific Committee on the Effects of Atomic Radiation, Sources and effects of ionizing radiation. Rep. General Assembly.,” Sources, United Nation, New York, 654., 2000. [12] Sinha, R.M., Shrivastava, V.K., Sarma, G.V.G., & Parthasarathy, T.N. (1995). Geological favourability for unconformity-related uranium deposits in northern parts of the Cuddapah basin: evidences from Lambapur uranium occurrence, Andhra Pradesh, India. Exploration and Research for Atomic Minerals, 111-126. [13] Verma, Mohan. (2009). Srisailam sub-basin, an uranium province of unconformity-related deposits in Andhra Pradesh – case study of Chitrial uranium exploration, Nalgonda District M. B. Verma1,*, P. B. Maithani2, A. Chaki2, P. Nageshwar Rao2 and Prakher Kumar3. Current science. 96. 588-591. [14] Jeyagopal, A.V., Kumar, Prakhar, & Sinha, R.M. (Dec 1996). Uranium mineralization in the Palnad sub-basin, Cuddapah basin, Andhra Pradesh, India. Current Science (Bangalore), 71(12), 957-959. [15] P. R. J. Parihar, “Cuddapah Basin e A Uranium province,” Explor. Atomic Minerals 22, pp. 1-19, 2012. [16] Rajaraman, H. & Veldi, Ramesh Babu & Dandele, P. & Chavan, S. & Achar, K. & Babu, P V. (2011). Using VLF-EM to delineate a fracture zone in basement granites for uranium exploration. The Leading Edge. 30. 1158-1161. 10.1190/1.3657076. [17] Minty, B.R.S., Luyendyk, A.P.J., & Brodie, R.C. (1997). Calibration and data processing for airborne gamma-ray spectrometry. AGSO Journal of Australian Geology and Geophysics, 17(2), 51-62. [18] Rezaei M, Ashoor M, Sarkhosh L. Airborne gamma ray spectrometry improvement using autoregressive integrated moving average model. IJRSM. 2018; 6 (2) :33-44 [19] Borhani M., Ghassemian H. (2014) Novel Spatial Approaches for Classification of Hyperspectral Remotely Sensed Landscapes. In: Movaghar A., Jamzad M., Asadi H. (eds) Artificial Intelligence and Signal Processing. AISP 2013. Communications in Computer and Information Science, vol 427. Springer, Cham. [20] J. G. R. Hovgaard, “Reducing statistical noise in airborne gamma-ray data through spectral component analysis. In: Gubins, A.G. (Ed.), Proceedings of Exploration97,” Fourth Decennial Conference on Mineral Exploration, p. pp. 753e764., 1997. [21] Borhani M., Ghassemian H. (2014) Kernel Grouped Multivariate Discriminant Analysis for Hyperspectral Image Classification. In: Movaghar A., Jamzad M., Asadi H. (eds) Artificial Intelligence and Signal Processing. AISP 2013. Communications in Computer and Information Science, vol 427. Springer, Cham [22] IAEA, “Airborne Gamma Ray Spectrometry Surveying,,” International Atomic Energy Agency p. p. 96., 1991. Technical Reports Series No. 323. Austria, Vienna,, [23] M. Borhani and H. Ghassemian, "Kernel Multivariate Spectral–Spatial Analysis of Hyperspectral Data," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, pp. 2418-2426, June 2015, doi: 10.1109/JSTARS.2015.2399936 [24] Mamdani, E. H.,(1976), “Advances in the linguistic synthesis of fuzzy controllers,” Int. J. Man-Mach. Stud., vol. 8, pp. 669–678.