Использование статистических методов в географии

Тип работы:
Курсовая
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Физико-математические науки
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54

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Содержание

1. Содержание изучаемой сферы деятельности. География

2. История внедрения математических исследований в географии

3. Основные математические методы обработки и анализа данных в географии

4. Наиболее интересные статистические исследования, проведенные в изучении географии, и полученные результаты на конкретных примерах

5. Перспективы и опыт компьютеризации исследований

Список литературы

1. Пяткин В. П. Непараметрический статистический подход к задаче обнаружения некоторых структур на аэрокосмических изображениях //Наукоемкие технологии.- 2002.- № 3. — С. 52−58

2. Рабинер Л., Гоулд Б. Теория и применение цифровой обработки сигналов, Москва: Мир, 1978.

3. Розанов Ю. А. Марковские случайные поля, М., 1981. — 256 с.

4. Розенфельд A. Распознавание и обработка изображений с помощью вычислительных машин: Пер. с англ. — М.: «Мир», 1972. — 230 с, ил.

5. Amir Z. Averbuch Michael V. Zheludev Unmixing and target recognition for hyperspectral images, School of Computer Science Tel Aviv University

6. Amir Z. Averbuch Michael V. Zheludev Supervision classi_cation and the orthogonal rotation algorithms for target recognition in hyperspectral images, School of Computer Science Tel Aviv University

7.R.R. Coifman, M. Maggioni, Di_usion wavelets, Appl. Comput. Harmon. Anal., in press.

8.C. Bateson, G. Asner, and C. Wessman, Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis, IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 10 831 094, Mar. 2000.

9.R. Seidel, Convex Hull Computations. Boca Raton, FL: CRC, 1997, ch. 19, pp. 361 375.

10.M. E. Winter An algorithm for fast autonomous spectral end-member determination in hyperspectral data, in Proc. SPIE Conf. Imaging Spectrometry V, 1999, pp. 266 275.

11.J. Boardman, F. A. Kruse, and R. O. Green, Mapping target signatures via partial unmixing of AVIRIS data, in Summaries 5th JPL Airborne Earth Science Workshop, vol. 1, 1995, pp. 2326.

12.J. Theiler, D. Lavenier, N. Harvey, S. Perkins, and J. Szymanski, Using blocks of skewers for faster computation of pixel purity index, In Proc. of the SPIE International Conference on Optical Science and Technology, volume 4132, pages 61{71, 2000.

13.A. Ifarraguerri and C. I. Chang, Multispectral and hyperspectral image analysis with convex cones, IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 756 770, Mar. 1999.

14.J. Boardman, Automating spectral unmixing of AVIRIS data using convex geometry concepts, in Summaries 4th Annu. JPL Airborne Geoscience Workshop, vol. 1, 1993, JPL Pub. 93−26, pp. 1114.

15.M. D. Craig, Minimum-volume transforms for remotely sensed data, IEEE Trans. Geosci. Remote Sens., vol. 32, no. 1, pp. 99 109, Jan. 1994.

16.C. Bateson, G. Asner, and C. Wessman, Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis, IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 10 831 094, Mar. 2000.

17.R. Seidel, Convex Hull Computations. Boca Raton, FL: CRC, 1997, ch. 19, pp. 361 375.

18.M. E. Winter An algorithm for fast autonomous spectral end-member determination in hyperspectral data, in Proc. SPIE Conf. Imaging Spectrometry V, 1999, pp. 266 275.

19.J. Boardman, F. A. Kruse, and R. O. Green, Mapping target signatures via partial unmixing of AVIRIS data, in Summaries 5th JPL Airborne Earth Science Workshop, vol. 1, 1995, pp. 2326.

20.J. Theiler, D. Lavenier, N. Harvey, S. Perkins, and J. Szymanski, Using blocks of skewers for faster computation of pixel purity index, In Proc. of the SPIE International Conference on Optical Science and Technology, volume 4132, pages 61{71, 2000.

21.D. Lavenier, J. Theiler, J. Szymanski, M. Gokhale, and J. Frigo, FPGA implementation of the pixel purity index algorithm, in Proc. SPIE Photonics East, Workshop on Recongurable Architectures, 2000.

22.J. H. Bowles, P. J. Palmadesso, J. A. Antoniades, M. M. Baumback, and L. J. Rickard, Use of vectors in hyperspectral data analysis, in Proc. SPIE Conf. Infrared Spaceborne Remote Sensing III, vol. 2553, 1995, pp. 148 157.

23.J. H. Bowles, J. A. Antoniades, M. M. Baumback, J. M. Grossmann, D. Haas, P. J. Palmadesso, and J. Stracka, Real-time analysis of hyperspectral data sets using NFLs orasis algorithm, in Proc. SPIE Conf. Imaging Spectrometry III, vol. 3118, 1997, pp. 3845.

24.J. M. Grossmann, J. Bowles, D. Haas, J. A. Antoniades, M. R. Grunes, P. Palmadesso, D. Gillis, K. Y. Tsang, M. Baumback, M. Daniel, J. Fisher, and I. Triandaf, Hyperspectral analysis and target detection system for the Adaptative Spectral Reconnaissance Program (ASRP), in Proc. SPIE Conf. Algorithms for Multispectral and Hyperspectral Imagery IV, vol. 3372, 1998, pp. 213.

25.M. P. Nascimento and M. Bioucas-Dias. Vertex component analysis: A fast algorithm to unmix hyperspectral data. IEEE Trans. Geosci. Remote Sensing, 43(4): 898{910, 2005.

26.C. I. Chang. Hyperspectral Imaging: Techniques for spectral detection and classication. Kluwer Academic, New York, 2003.

27.C. I. Chang, X. Zhao, M. L. G. Althouse, and J. J. Pan. Least squares subspace projection approach to mixed pixel classication for hyperspectral images. IEEE Trans. Geosci. Remote Sensing, 36(3): 898{912, 1998.

28.J. Settle, On the relationship between spectral unmixing and subspace projection, IEEE Trans. Geosci. Remote Sens., vol. 34, no. 4, pp. 1045 1046, Jul. 1996.

29.J. C. Harsanyi and C.I. Chang. Hyperspectral image classication and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans. Geosci. Remote Sensing, 32(4): 779{785, 1994.

30.Y. H. Hu, H. B. Lee, and F. L. Scarpace. Optimal linear spectral unmixing. IEEE Trans. Geosci. Remote Sensing, 37: 639{644, 1999.

31.L. O. Jimenez and D. A. Landgrebe. Hyperspectral data analysis and supervised feature reduction via projection pursuit. IEEE Trans. Geosci. Remote Sensing, 37(6): 2653{2664, 1999.

32.A. S. Mazer, M. Martin, et al. Image processing software for imaging spectrometry data analysis. Rem. Sens. of the Environ., 24(1): 201{210, 1988.

33.J. J. Settle. On the relationship between spectral unmixing and subspace projection. IEEE Trans. Geosci. Remote Sensing, 34: 1045{1046, 1996.

34.R. H. Yuhas, A. F. H. Goetz, and J. W. Boardman. Discrimination among semi-arid landscape endmembres using the spectral angle mapper (SAM) algorithm. In Summaries of the 3rd annu. JPL Airborne Geosci. Workshop, R. O. Green, Ed. Publ., 92−14, volume 1, pages 147{149, 1992.

35.P. Common. Independent component analysis: A new concept. Signal Processing, 36: 287{314, 1994.

36.A. Hyvarinen, J. Karhunen, and E. Oja. Independent Component Analysis. John Wiley & Sons, Inc., 2001.

37.J. D. Bayliss, J. A. Gualtieri, and R. F. Cromp. Analysing hyperspectral data with independent component analysis. In Proc. of the SPIE conference 26th AIPR Workshop: Exploiting New Image Sources and Sensors, volume 3240, pages 133{143, 1997. 29

38.V. Botchko, E. Berina, Z. Korotkaya, J. Parkkinen, and T. Jaaskelainen. Independent component analisys in spectral images. In Proc. of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation, pages 203{207, 2003.

39.N. Keshava, J. Kerekes, D. Manolakis, and G. Shaw. An algorithm taxonomy for hyperspectral unmixing. In Proc. of the SPIE AeroSense Conference on Algorithms for Multispectral and Hyperspectral Imagery VI, volume 4049, pages 42{63, 2000.

40.L. Parra, K.R. Mueller, C. Spence, A. Ziehe, and P. Sajda. Unmixing hyperspectral data. Advances in Neural Information Processing Systems, 12: 942{948, 2000.

41.T. M. Tu. Unsupervised signature extraction and separation in hyperspectral images: Anoise-adjusted fast independent component analysis approach. Optical Engineering of SPIE, 39(4): 897{906, 2000.

42.J. C. BURGES A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2, 121 167 (1998)

43.R. O. Duda, P. E. Hart, D. G. Stork Pattern Classication. optimized DJVU le with searchable text. 2ed., Wiley, 2000

44.H. Attias. Independent factor analysis. Neural Computation, 11(4): 803{851, 1999.

45.E. Moulines, J.F. Cardoso, and E. Gassiat. Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models. In Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, volume 5, pages 3617{3620, 1997.

46.M. P. Nascimento and M. Bioucas-Dias. Does independent component analysis play a role in unmixing hyperspectral data? IEEE Trans. Geosci. Remote Sensing, 43(1): 175{187, 2005.

47.M. P. Nascimento and M. Bioucas-Dias. Dependent Component Analysis: A Hyperspectral Unmixing Algorithm, in Proceedings of the 3rd IbPRIA, ser. LNCS, vol. 4478. Springer-Verlag, pp. 612{619, Girona, Spain, June 2007.

48.C.H. Chen and X. Zhang. Independent component analysis for remote sensing study. In Proc. of the SPIE Symp. on Remote Sensing Conference on Image and Signal Processing for Remote Sensing V, volume 3871, pages 150{158, 1999.

49.S. -S. Chiang, C.I. Chang, and I. W. Ginsberg. Unsupervised hyperspectral image analysis using independent component analysis. In Proc. of the IEEE Int. Geosci. and Remote Sensing Symp., 2000.

50.E.J. Kelly, An adaptive detection algorithm, IEEE Trans. Aerosp. Electron. Syst., vol. 22, pp. 115{127, March 1986.

51.E.J. Kelly, Adaptive detection in non-stationary interference, part III, MIT Lincoln Laboratory, Lexington, MA, Tech. Rep. 761, 1987.

52. teory. narod. ru/history. htm

53. geoman. ru/geography/item/f00/s10/e0010450/index. shtml

54. www. sovzond. ru/software/90/

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