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Wykorzystanie granulometrii obrazowej w klasyfikacji treści zdjęć satelitarnych

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Warianty tytułu
EN
Image granulometry and its utilisation in satellite images classification
Języki publikacji
PL
Abstrakty
PL
W pracy przedstawiono nową metodę klasyfikacji treści zdjęć satelitarnych, opartą na wykorzystaniu granulometrycznej analizy tekstury obrazu. Opisano podstawy teoretyczne zaprezentowanej metody oraz zbadano jej dokładność, w zależności od wybranych parametrów przetworzeń granulometrycznych oraz cech obrazów źródłowych. Porównano ją także z innymi, dotychczas stosowanymi metodami klasyfikacji treści zdjęć satelitarnych. Istotą zaproponowanej metody jest wykorzystanie, oprócz danych spektralnych, również map granulometrycznych, czyli obrazów zawierających informację na temat tekstury obrazu w otoczeniu poszczególnych pikseli, powstających w wyniku granulometrycznych przetworzeń obrazu. Ważną zaletą granulometrii obrazowej jako metody oznaczania tekstury obrazu jest, m.in., wieloskalowość, czyli możliwość określania stopnia tekstury o rożnych rozmiarach ziarna. Drugą kluczową zaletą jest prawidłowe działanie również na krawędziach obiektów na obrazie, czyli odporność na tzw. błąd krawędzi. Przedstawiona metoda klasyfikacji polegająca na złożeniu map granulometrycznych i oryginalnych obrazów wielospektralnych pozwala uwzględniać kontekstową cechę interpretacyjną - teksturę, zwiększając możliwości klasyfikacji, a jednocześnie cechuje się dużą prostotą wykonania, podobną do klasycznej pikselowej klasyfikacji spektralnej. Efektywność granulometrii obrazowej zbadano pod kątem kilku czynników: rozdzielczości przestrzennej i rodzaju obrazu źródłowego, rodzaju morfologicznych operacji otwarcia i domknięcia oraz rozmiaru okna granulometrii określającego przestrzenny zasięg obliczenia lokalnej granulometrii względem poszczególnych pikseli. W pierwszej kolejności przeanalizowano separatywność wybranych klas pokrycia lub użytkowania terenu na podstawie wyłącznie danych spektralnych, a także na podstawie map granulometrycznych. W wybranych przypadkach, dzięki zastosowaniu analizy granulometrycznej, stwierdzono znaczny wzrost separatywności klas. Główna część pracy koncentruje się na badaniu dokładności klasyfikacji wykonanej przy użyciu zaproponowanej metody. Uzyskane wyniki dowodzą, że wykorzystanie map granulometrycznych w procesie klasyfikacji może znacząco podnieść jej dokładność. Stwierdzono przy tym istotny wpływ rozdzielczości obrazu źródłowego na efektywność badanej metody. Określono i opisano również znaczenie pozostałych, przedstawionych wyżej parametrów przetworzeń granulometrycznych, i samej klasyfikacji. Wnioski z badań pozwoliły na przedstawienie propozycji modelu dwuetapowej klasyfikacji wykorzystującej zarówno wyniki klasyfikacji spektralnej, jak i spektralno-teksturowej, co pozwoliło na uzyskanie optymalnej dokładności. Zaproponowana metoda może być stosowana w procesie półautomatycznego tworzenia map pokrycia lub użytkowania terenu na podstawie zdjęć satelitarnych lub lotniczych, pozwalając uzyskiwać większa dokładność, niż klasyfikacja w podejściu spektralnym.
EN
This book presents a new method of classification of satellite images, based on utilisation of granulometric analysis of image texture. The theoretical background of the method and its accuracy, depending on different parameters of granulometric processing and input images, is presented. It is compared to other approaches of satellite image classification. The essence qf the method relies on the use of granulometric maps, i.e. images containing information about a local texture in every pixel, additionally to spectral data contained in original multispectral images. One of the main advantages of the proposed method is its multiscality, i.e. a possibility to define a texture of an image, depending on a different size of texture element. Also, granulometric analysis of a texture is resistant to the so-called border error. As a result, it works properly on the edges of objects in an image. The method, based on a combination of granulometric maps and multispectral images. allows to take into account an important contextual feature of an image - that is, texture. Consequently, it is increasing a potential for correct classification, while remaining as simple as a pixel-based spectral classification approach. The effectiveness of image granulometry has been tested with different features and parameters: spatial resolution and a type of an input image, type of morphological opening and closing, as well as the size of a granulometric window, defining a range of a local granulometric analysis. A separability of different classes of land cover or land use, basing on spectral data and granulemetric maps, has been tested. Significant increase of separabillity has been observed in certain cases. The main goal of the book was to study accuracy of classification, basing on the presented method. The results of the research show that a use of granulometric maps in a classification process may increase the accuracy significantly. An important influence of input image's spatial resolution on the outcome has been observed. Also, the impact of other aforementioned features has been tested and described. Conclusions derived from the research allow to propose a two-step model, using results of both, spectral and spectro-textural classifications, to obtain an optimal accuracy of classification. The presented method may be used in process of semi-automatic generation of land cover and land use maps, basing on satellite or aerial images, obtaining accuracy level, which is higher than in the case of a spectral-based classification.
Rocznik
Tom
Strony
3--271
Opis fizyczny
Bibliogr. 228 poz., rys., tab., wykr.
Twórcy
autor
  • Wydział Geodezji i Kartografii
Bibliografia
  • 1. Abry P., Wendt H., Jaffard S. (2012). When Van Gogh meets Mandelbrott: Multifractal classification of painting's texture. Signal Processing. Image Processing for Digital Art Work, 93(3), 554-572.
  • 2. Ahearn S.C. (1988). Combining Laplacian images of different spatial frequencies (scales): implications for remote sensing image analysis. IEEE Transactions on Geoscience and Remote Sensing, 26(6), 826-831.
  • 3. Arai K., (1993). A classification method with a spatial-spectral variability. International Journal of Remote Sensing, 14, 699-709.
  • 4. Arivazhagan S., Ganesan L., Priyal S.P. (2006). Texture classification using Gabor wavelets based rotation invariant features. Pattern Recognition Letters, 27, 1976-1982.
  • 5. Arivazhagan S., Ganesan L. (2003). Texture segmentation using wavelet transform. Pattern Recognition Letters, 24, 3197-3203.
  • 6. Ball G.H., Hall D.J. (1965). Isodata, a method of data analysis and pattern classification, Stanford Research Institute, Menlo Park, United States. Office of Naval Research. Information Sciences Branch, ss. 61.
  • 7. Baraldi A., Parmiggiani F. (1995). An Investigation of the Textural Charateristics Associated with Gray Level Coocurrence Matrix Statistical Parameters. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 293-304.
  • 8. Benediktsson J.A., Swain P.H., Ersoy O.K. (1990). Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 540-552.
  • 9. Beucher S., Lantuejoul C. (1979). Use of Watershed in Contour Detection. International Workshop on image processing: Real-time Edge and Notion detection/estimation, 17-21 września 1979, Rennes, Francja, 2.1-2.12.
  • 10. Bian L. (2003). Retrieving Urban Objects Using a Wavelet Transform Approach, Photogrammetric Engineering and Remote Sensing, 69(2), 13 3-141.
  • 11. Bianconi F., Fernández A. (2007). Evaluation of the effects of Gabor filter parameters on texture classification. Pattern Recognition, 40, 3325-3335 .
  • 12. Blake A., Kohli P. (2011). Introduction to Markov Random Fields. Markov Random Fields for Vision and Image Processing. (red.) Blake A., Kohli P., Rother C., Massachusetts Institute of Technology, 1-15.
  • 13. Blaschke T. (2010). Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1), 2-16.
  • 14. Blaschke T., Lang S., Lorup E., Strobl J., Zeil P. (2000), Object-Oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications, Environmental information for planning, politics and the public, 2, 555-570.
  • 15. Blaschke T., Strobl J. (2001). What's wrong with pixels? Some recent developments interfacing remote sensing and GIS. GeoBIT/GIS, 6(1), 12-17.
  • 16. Borkowski A., Bujakiewicz A., Ewiak I., Kaczyński R., Pyka K. (2012). Stan obecny i kierunki rozwoju fotogrametrii, teledetekcji i GIS w świetle XXII Kongresu ISPRS. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 24, 31-51.
  • 17. Bosch E.H. Texture information and supervised classification of hyperspectral imagery by means of neural networks. In GeoComputation 99, 1999. Online http://www.geocomputation.org/1999/019/gc_019.htm
  • 18. Braathen B., Pieczyński W. Masson P. (1993). Global and local methods of unsupervised Bayesian segmentation of images. Machine Graphics and Vision, 2(1), 39-52.
  • 19. Breiman L. (2001). Random Forests. Machine Learning, 45, 5-32.
  • 20. Bruzzone L., Conese C., Maselli F., Rol F. (1997). Multisource Classification of Complex Rural Areas by Statistical and Neural-Network Approaches. Photogrammetric Engineering & Remote Sensing, 63(5), 523-533.
  • 21. Budreski K.A., Wynne R.H., Browder J.O., Campbell J.B. (2007). Comparison of Segment and Pixel-based Non-parametric Land Cover Classification in the Brazilian Amazon Using Multitemporal Landsat TM/ETM+ Imagery. Photogrammetric Engineering and Remote Sensing, 73(7), 813-827.
  • 22. Buldyrev S.V., Goldberger A.L., Havlin S., Peng C.K., Stanley H.E. (1995). Fractals in Biology and Medicine: From DNA to the Heartbeat. Fractals in Science (red.) Bunde A., Havlin S., Springer, 49-88.
  • 23. Burrough P.A., (1993). Soil Variability: A Late 20th Century View, Soils and Fertilizers, 529-562.
  • 24. Campbell J.B. (2008). Introduction to Remote Sensing Fourth Edition. The Guilford Press, Londyn, ss. 626.
  • 25. Carbone A., Gromov M., Prusinkiewicz P. (2000). Pattern formation in biology, vision and dynamics. World Scientific. ss. 78.
  • 26. Çesmeli E., Wang D.L. (2001), Texture Segmentation Using Gaussian-Markov Random Fields and Neural Oscillator Networks. IEEE Transactions on Neural Networks, 12(2), 394-404.
  • 27. Chanussot J., Benediktsson J.A., Fauvel M. (2006). Classification of Remote Sesing Images From Urban Areas Using a Fuzzy Possibilistic Model. IEEE Geoscience and Remote Sensing Letters, 3(1), 40-44.
  • 28. Chaudhuri B., Sarkar N. (1995). Texture Segmentation Using Fractal Dimension. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1), 72-77.
  • 29. Chávez P., Yarlequé C., Posadas A., Mares V., Loyaza H., Chuquillanqui C., Zorogastúa P., Flexas J., Quiroz R. (2010). Applying Multifractal Analysis to Remotely Sensed Data for Assessing PYVV Infection in Potato (Solanum tuberosum L.) Crops. Remote Sensing, 2, 1197-1216.
  • 30. Chen J., Chen D., Blostein D. (2007). Wavelet-Based Classification of Remotely Sensed Images: A Comparative Study of Different Feature Sets in Urban Environment. Journal of Environmental Informatics 10(1), 2-9.
  • 31. Chmiel J. (2002). Przykład wykorzystania sieci neuronowych w cyfrowej klasyfikacji pokrycia terenu. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 12a, 63-78.
  • 32. Chmiel J., Fijałkowska A., Woronkiewicz L. (2007), Cyfrowa analiza zdjęcia satelitarnego VHR dla pozyskiwania danych o pokryciu terenu -podejście obiektowe i pikselowe. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 17a, 139-148.
  • 33. Chmiel J., Fijałkowska A. (2008). Geo-object based VHR image classification supported by GIS layers and expert knowledge, in: G.J. Hay T. Blaschke and D. Marceau (Eds.). GEOBIA 2008 - Pixels, Objects, Intelligence. GEOgraphic Object Based Image Analysis for 21st Century. University of Calgary, Calgary Alberta, Canada, 05-08 August 2008. ISPRS Vol. No. XXXVIII-4/C1, Archives.
  • 34. Chmiel J., Fijałkowska A. (2010a). Thematic accuracy assessment for object based classification in agriculture areas: comparative analysis of selected approaches, in E.A. Addink and F.M.B. Van Coillie (Eds.) - GEOBIA 2010 Geographic Object-Based Image Analysis. Ghent University, Ghent, Belgium, 29 June - 2 July. ISPRS Vol. No. XXXVI11-4/C7, Archives.
  • 35. Chmiel J., Fijałkowska A. (2010b). Evaluation of influence of image segmentation parameters and other selected effects on thematic accuracy of object oriented VHRS image analysis, in: Manakos I. and Kalaitzidis C. (red.) Imaging Europe, 51-58.
  • 36. Chmiel J., Cybo J., Wypart W., Suchoń J. (1995). Analiza multifraktalna struktury kokilowego odlewu walca hutniczego. Solidification of Metals and Alloys, 22, 70-75.
  • 37. Clarke K.C., 1986. Computation of the Fractal Dimension of Topographic Surfaces Using the Triangular Prism Surface Area Method, Computers and Geosciences, 12(5), 713-722.
  • 38. Clausi D.A. (2002). An analysis of co-occurrence texture statistics as a function of grey-level quantization. Canadian Journal of Remote Sensing, 28(1), 45-62.
  • 39. Cochran W.G. (1963). Sampling Techniques, 2nd edition, John Wiley & Sons, Nowy Jork, ss. 413.
  • 40. Cohen J. (1960). A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20(1), 37-46.
  • 41. Congalton R. (1991). A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37, 35--46.
  • 42. Congalton R.G., Oderwald R.G., and Mead R.A. (1983). Assessing Landsat classification accuracy using discrete multivariate statistical techniques, Photogrammetry Engineering and Remote Sensing 49(12), 1671-1678.
  • 43. Congalton R.G. (1988a). Using spatial autocorrelation analysis to explore errors in maps generated from remotely sensed data, Photogrammetry Engineering and Remote Sensing, 54(5), 587-592.
  • 44. Congalton R.G. (1988b). A comparison of sampling schemes used in generating error nmtrices for assessing the accuracy of maps generated from remotely sensed data, Photogrammetry Engineering and Remote Sensing 54(5), 593-600.
  • 45. Corcoran P., Winstanley A., Mooney P. (2010). Segmentation performance evaluation for object-based remotely sensed image analysis. International Journal of Remote Sensing, 31(3), 617-645.
  • 46. Cushnie J.L. (1987). The interactive effect of spatial resolution and degree of internal variability within land-cover types on classification accuracies, International Journal of Remote Sensing, 8, 12-29.
  • 47. Dalka P., Czyżewski (2014). Rozpoznawanie ruchów i gestów wykonywanych ustami w obrazie wizyjnym z użyciem sieci neuronowych. Sieci neuronowe w inżynierii biomedycznej. red. Tadeusiewicz R., Korbicz J., Rutkowski L., Duch W., Akademicka Oficyna Wydawnicza EXIT, Warszawa, 461-487.
  • 48. Darling E.M., Joseph R.D. (1968). Pattern recognition from satellite altitudes. IEEE Trans. Syst., Man, Cybern., vol. SMC-4, 30-47.
  • 49. Daubechies I. (1992). Ten Lectures on Wavelets. SIAM, ss. 377.
  • 50. Day C. (1997). Remote sensing applications which may be addressed by neural networks using parallel processing technology, In Kanellopoulos I., Wilkinson G.G., Roli F., Austin J. (eds) Neuro-computation in Remote Sensing Data Analysis, Springer, Berlin, 262-279.
  • 51. De Jong S.M., Burrough P.A. (1995). A Fractal Approach to the classification of Mediterranean Vegetation Types in Remotely Sensed Images, Photogrammetric Engineering and Remote Sensing, 61, 1041-1053.
  • 52. Derin H., Elliott H. (1987). Modelling and segmentation of noisy textured images using Gibbs random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI, 9(1), 39-55.
  • 53. Dey V., Zhang Y., Zhong M. (2010). A review on image segmentation techniques with remote sensing perspective. Wagner W., Szekely B. (red.): ISPRS TC VII Symposium - 100 Years ISPRS, 31-42.
  • 54. Dougherty E.R., Pelz J.B., Sand F., Lent A. (1992). Morphological Image Segmentation by Local Granulometric Size Distributions, Journal of Electronic Imaging, 1(1), 46-60.
  • 55. Drzewiecki W., Wawrzaszek A., Krupiński M., Bernat K. (2013). Comparison of Selected Textural Features as Global Contet-Based Descriptors of VHR Satellite Image - the EROS-A Study. Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, 43-49.
  • 56. Duda D., Krętowski M., Bézy-Wendling J. (2007). Ekstrakcja cech teksturalnych w klasyfikacji obrazów tomograficzych wątroby. Zeszyty Naukowe Politechniki Białostockiej 2007 Informatyka - Zeszyt 2, 51-66.
  • 57. Duda R.O., Hart P.E., Stork D.G. (2001). Pattern Classification. Second Edition. Wiley Interscience, ss. 654.
  • 58. Edwards G., Landary R., Thomson K.P.B. (1988). Texture analysis of forest regeneration sites in high-resolution SAR imagery. Proceedings of the International Geosciences and Remote Sensing Symposium (IGARSS 88), ESA SP-284 (Paris: European Space Agency), 1355-1360.
  • 59. Esch T., Thiel M., Bock M., Dech S. (2008). Improvement of Image Segmentation Accuracy Based on Multiscale Optimization Procedure. IEEE Geoscience and Remote Sensing Letters, 5(3), 463-467.
  • 60. Faber A., Förstner W. (1999). Scale characteristics of local autocovariances for texture segmentation. International Archives of Photogrammetry and Remote Sensing, 32, 7-4-3/W6.
  • 61. Falconer K. (2003). Fractal Geometry: Mathematical Foundations and Applications. John Wiley, New York, ss. 366.
  • 62. Fisher L., van Ness J.W. (1971). Admissable clustering procedures, Biometrika, 58, 91-104
  • 63. Flanders D., Hall-Beyer M., Pereverzoff J. (2003). Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Canadian Journal of Remote Sensing, 29(4), 441-452.
  • 64. Flouzat G., Amram O., Laporterie F., Cherchali S. (2001). Multiresolution analysis and reconstruction by a morphological pyramid in the remote sensing of terrestrial surfaces. Signal Processing, 81(10), 2171-2185.
  • 65. Foody G.M. (2008). Sample Size Determination for Image Classification Accuracy Assessment and Comparison. Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 154-162.
  • 66. Foody G.M. (1996). Relating the land-cover composition of mixed pixels to artificial neural network classification output. Photogrammetric Engineering and Remote Sensing, 62, 491-499.
  • 67. Foody G.M., Arora M.K. (1997). An evaluation of some factors affecting the accuracy of classification by an artificial neural network. International Journal of Remote Sensing, 18, 799-810.
  • 68. Foody G.M. (2006). Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks. Ecological Informatics. Scope, Techniques and Applications. Friedrich Recknagel (red.), Springer, ss. 496.
  • 69. Galloway M. (1974). Texture analysis using gray level run lengths. Computer Graphics Image Processing, 172-199.
  • 70. Gawlik J., Magdziarczyk W., Wojnar L. (2011). Analiza fraktalna struktury geometrycznej powierzchni. Komputerowo Zintegrowane Zarządzanie. Kosnala R. (red.), Oficyna Wydawnicza Polskiego Towarzystwa Zarządzania Produkcją, Opole, 382-396.
  • 71. Giannini M.B., Merola P., Allegrini A. (2012). Texture Analysis for Urban Areas Classification in High Resolution Satellite Imagery. Applied Remote Sensing Journal, 2(2), 65-71.
  • 72. Gomez M., Salinas R.A. (2006). A New Technique for Texture Classification Using Markov Random Fields. International Journal of Computers, Communications & Control, 1(2), 44-51.
  • 73. Gong Y., Shu N., Li J., Lin L., Li X. (2010). A new conception of image texture and remote sensing image segmentation based on Markov random field. Geo-spatial Information Science, 13(1), 16-23.
  • 74. Gonzalez R.C. and Woods R.E. (2001). Digital Image Processing, Prentice Hall, NJ, ss. 349.
  • 75. Gopal S., Woodcock C. (1996). Remote Sensing of Forest Change Using Artificial Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 34(2), 398-404.
  • 76. Goumehei E. (2010) Contextual image classification with Support Vector Machine. Msc thesis, University of Twente.
  • 77. Gouyet J.F. (1996). Physics and fractal structures. Paris/New York: Masson Springer, ss. 234.
  • 78. Graham M.E. (2008). Evaluating accuracy issues in mapping benthic habitats: An investigation in the causes of misclassification an the importance of segmentation parameters. University of New Hampshire, ss. 113.
  • 79. Greenspan H., Goodman R.M., Chellappa R. (1991). Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery, in John E. Moody; Stephen Jose Hanson & Richard Lippmann, (red.), ‘NIPS', Morgan Kaufmann, 444-451.
  • 80. Greenspan H.K., Goodman R. (1993). Remote sensing image analysis via a texture classification neural network. in Advances in Neural Information Processing Systems 5, Hanson S.J., Cowan J.D., Giles C.L. (red.), Morgan Kaufmann, 425-432.
  • 81. Gurney C.M., Townshend J.R.G. (1983). The Use of Contextual Information in the Classification of Remotely Sensed Data Photogrammetric Engineering and Remote Sensing, 49, 55-64.
  • 82. Haar A. (1910). Zur Theorie der orthogonalen Funktionensysteme, Mathematische Annalen 69 (3), 331-371.
  • 83. Haas A., Matheron G., Serra J. (1967). Morphologie Mathématique et granulométries en place. Part I. Annales des Mines 11, 736-753.
  • 84. Haas A., Matheron G., Serra J. (1967). Morphologie Mathématique et granulométries en place. Part II. Annales des Mines 12, 768-782.
  • 85. Han J. Ma K.K. (2007). Rotatio-invariant and scale-invariant Gabor features for texture image retrieval. Image and Vision Computing 25, 1474-1481.
  • 86. Hao W., Yurong S., Wenzhong S., Xiaoling C., Dongjie F. (2013). Examining the Satellite-Detected Urban Land Use Spatial Patterns Using Multidimensional Fractal Dimension Indices, Remote Sensing, 5(10), 5152-5172.
  • 87. Haralick R.M. (1979). Statistical and Structural Approaches to Texture. Proceedings Of The IEEE, 67(5), 786-804.
  • 88. Haralick R.M., Shanmugam K., Dinstein I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-3, nr 6, 610-621.
  • 89. Haralick R.M., Sternberg S.R., Zhuang X. (1987). Image Analysis using Mathematical Morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4), 532-550.
  • 90. Harte D. (2001). Multifractals. Theory and Applications. Chapman & Hall/CRC Press, Boca Raton, Londyn, Nowy Jork, Waszyngton, ss. 248.
  • 91. Hay A.M. (1979). Sampling designs to test land-use map accuracy, Photogrammetry Engineering and Remote Sensing, 5(4), 529-533.
  • 92. Hay G.J., Castilla G. (2006). Object-Based Image Analysis: Strengths, Weaknesses, Opportunities And Threats (SWOT). OBIA 2006: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Lang L., Blaschke T., Schöpfer E. (red.).
  • 93. Hay G.J., Castilla G. (2008). Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sensing Applications. 75-89.
  • 94. Hay G.J., Castilla G., Wulder M.A., Ruiz J.R. (2005). An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation, 7, 339-359.
  • 95. Hazewinkel M., red. (2001), Haar system. w Encyclopedia of Mathematics, Springer.
  • 96. Hejmanowska B. (2007). Porównanie wyników klasyfikacji obrazów satelitarnych Hyperion i ALI. Archiwum Fotegrametrii, Kartografii i Teledetekcji, 17a, 1-10.
  • 97. Hormann K. (2003). From Scattered Samples to Smooth Surfaces. Proceedings of the Fourth Israel-Korea Bi-National Conference on Geometric Modeling and Computer Graphics, Cohen-Or, D., Dyn, N., Elber, G., Shamir, A. (red.), 1-5.
  • 98. Hwang H.J., Lee K.(2006). Classification accuracy of wavelet-based fusion image with texture filtering using high resolution satellite images, [w:] Lang S., Blaschke T., Schöpfer E. (red.), 1st International Conference on Object-based Image Analysis (OBIA 2006), Workshop proceedings, Salzburg.
  • 99. Idrissa M., Acheroy M. (2002). Texture classification using Gabor filters. Pattern Recognition Letters, 23, 1095-1102.
  • 100. Iwaniak A., Krówczyńska M., Pałuszyński W. (2002). Użycie sieci neuronowych do klasyfikacji obszarów miejskich na zdjęciach satelitarnych. Geodesia et Descripto Terrarum, 1(1-2), 5-13.
  • 101. Jackson Q., Landgrebe D. (2002). Adaptive Bayesian Contextual Classification Based on Markov Random Fields. IEEE Transactions on Geoscience and Remote Sensing, 40(11), 2454-2463.
  • 102. Jensen J.R. (1996). Introductory Digital Image Processing - A Remote Sensing Perspective, Prentice Hall, NJ, ss. 316.
  • 103. Jiang Z.Y., Chen X.I., Li Y.S., Chen C.Q. (2004). A multi-scale segmentation method for remotely sensed images based on granulometry. ISPRS Archives - Volume XXXV Part B8, 46-52.
  • 104. Julesz B. (1962) Visual pattern discrimination. IRE Transactions on Information Theory, 8(2), 84-92.
  • 105. Kaczyński R. (2013). Trendy w fotogrametrii i teledetekcji w świetle XII Kongresu ISPRS. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 25, 77-83.
  • 106. Karperien A. (2002). What are Multifractals?, ImageJ, http://rsbweb nih.gov/ij/plugins/fraclac/FL-Help/Multifractals.htm, data dostępu: 2014-06-29.
  • 107. Kasprzak A. (2010). Analiza wielofraktalnej struktury czasów międzytransakcyjnych za pomocą modelu błądzenia przypadkowego w czasie ciągłym. Rozprawa doktorska, Uniwersytet Warszawski.
  • 108. Kekre H.B., Thepade S.D., Sarode T.K., Sutyyawanshi, V. (2010). Image Retrieval using Texture Features extracted from GLCM, LBG and KPE. International Journal of Computer Theory and Engineering, 2(5), 1793-8201.
  • 109. Kemmouche A., Mering C., Sansal B., Dewolf Y. (2004). Macro-texture mapping from satellite images by morphological granulometries: application to vegetation density mapping in arid and semi-arid areas. International Journal of Remote Sensing, 25(23), 5319-5335.
  • 110. Kettig R.L., Landgrebe D.A. (1976). Classification of Multispectral Image Data by Extraction and Classification of Homogenous Objects, IEEE Transactions on Geoscience Elektronics, 14(11), 19-26.
  • 111. Khedam R., Belhadj-Aissa A. (2001). A General Multisource Contextual Classification Model of Remotely Sensed Imagery based on MRF. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, 231-235.
  • 112. Khedam R., Belhadj-Aissa A. (2003). Study of ICM parameters influence on images satellite contextual classification. Geoinformation for European-wide Integration, Benes T. (red.), 79-85.
  • 113. Khedam R., Belhadj-Aissa A. (2004). Contextual Classification of Remotely Sensed Data Using Map Approach and MRF. ISPRS Archives - Volume XXXV Part 87, 11-16.
  • 114. Kiema J.B.K. (2000). Effect of wavelet compression on the automatic classification of urban environments using high resolution multispectral imagery and laser scanning data. International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B. Amsterdam , 488-495.
  • 115. Kindermann R., Snell J.L. (1980). Markov Random Fields and Their Applications. American Mathematical Society, ss. 142.
  • 116. Klinkenberg B., (1992). Fractals and Morphometric Measures: Is there a Relationship? Geomorphology, 5, 5-20.
  • 117. Krawczyk K., Winnicki I. (2012). Kompozycje barwne w interpretacji zachmurzenia konwekcyjnego. Biuletyn WAT 16(1), 9-29.
  • 118. Krawczyk K., Winnicki I., Jasiński J., Kroszczyński K., Pietrak S. (2012). Maski wybranych krawędziowych filtrów Laplace 'a w przetwarzaniu danych cyfrowych. Biuletyn WAT 17(1), 145-170.
  • 119. Kubik T., Paluszyński W., Iwaniak A., Tymków P. (2008). Klasyfikacja obrazów rastrowych z wykorzystaniem sztucznych sieci neuronowych i statystycznych metod klasyfikacji. Wydawnictwo Uniwersytetu Przyrodniczego we Wrocławiu, Wrocław, ss. 86.
  • 120. Kunsumanigrum R., Arymurthy A.M. (2011). Color and Texture Feature for Remote Sensing - Image Retrieval System: A Comparative Study, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 2, September, 2011, 125-135.
  • 121. Kupidura P. (2002). Badanie zmienności wskaźników roślinności na podstawie zdjęć. Praca magisterska, Politechnika Warszawska.
  • 122. Kupidura P. (2010). Semi-automatic method for a built-up area intensity survey using morphological granulometry. The Problem of Landscape Ecology, 38, 271-277.
  • 123. Kupidura P. (2014). Automatic extraction of built-up areas in satellite images using fractal analysis and morphological granulometry. 34th EARSeL Symposium 2014, 16-20 czerwca, Warszawa, referat.
  • 124. Kupidura P., Gwadera Ł. (2010). Comparison of different approaches to extract heterogeneous objects from an image using orchards example, w: International Archives of the Photogrammetry, Elsevier, vol. 38, nr 3B, 2010, 13-18.
  • 125. Kupidura P., Koza P., Marciniak J. (2010). Morfologia matematyczna w teledetekcji. Wydawnictwo Naukowe PWN, Warszawa, ss. 250.
  • 126. Lam N.S.N. (1990). Description and Measurement of Landsat TM Images Using Fractals, Photogrammetric Engineering and Remote Sensing, 56(2), 187-195.
  • 127. Lewiński S. (2006). Object-oriented classification of Landsat ETM+ satellite image. Journal of Water and Land Development, 10, 91-106.
  • 128. Lewiński S., Bochenek Z., Turlej K. (2010). Application of object-oriented method for classification of VHR satellite images using rule-based approach and texture measures. Geoinformation Issues, 2(1), 19-26.
  • 129. Lewiński S., Aleksandrowicz S. (2012). Ocena możliwości wykorzystania tekstury w rozpoznaniu podstawowych klas pokrycia terenu na zdjęciach satelitarnych różnej rozdzielczości. Archiwum Fotogrametrii i Teledetekcji, 23, 229-237.
  • 130. Lewiński S., Aleksandrowicz S., Banaszkiewicz M. (2014). Testing texture of VHR panchromatic data as a feature of land cover classification. Acta Geophysica, 63(2), 547-567.
  • 131. Lillesand T.M., Kiefer R. W. Chipman J W. (2004) Remote Sensing and Image Interpretation. Fifth Edition, Wiley, ss. 763.
  • 132. Liu Z.F., Sang. E.F. (2003). Texture image classification using multi-fractal dimension. Journal of Marine Science and Application, 2(2), 76-81.
  • 133. Lopes R., Betrouni N. (2009). Fractal and multifractal analysis: a review. Medical Image Analysis, 13, 634-649.
  • 134. Luo B., Aujol J.F., Gousseau Y. (2009). Local Scale Measure from the Topographic Map and Application to Remote Sensing Images. Multiscale Modeling & Simulation, 8(1), 1-29.
  • 135. Mallat S.G. (1989). A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.
  • 136. Mark D.M., Aronson P.B. (1984). Scale Dependent Fractal Dimensions of Topographic Surfaces: An Empirical Investigation with Applications in Geomorphology and Computer Mapping, Mathematical Geology, 16, 671-683.
  • 137. Maxwell S.K. (2010). Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application. Spat Spatiotemporal Epidemiol, 1(4), 231-237.
  • 138. McCloy K. R. (2005). Resource Management Information Systems: Remote Sensing, GIS and Modelling, Second Edition. CRC Press, ss. 616.
  • 139. Mering C., (2011). Mapping of The Ligneous Cover Change in The Sahel From High Resolution Panchromatic Images By Using Granulometric Analysis. Proceedings of ICS13, 149-155.
  • 140. Mering C., Baro J., Upgei E. (2010). Retrieving urban areas on Google Earth images: application to towns of West Africa. International Journal of Remote Sensing, 31(22), 5867-5877.
  • 141. Mering C., Callot Y., Kemmouche A. (1996). Analysis and Mapping of Natural Landscapes from Satellite Images Using Morphological Filters. Microscopy, Microanalysis, Microstructures, 7, 323-330.
  • 142. Mering C., Chopin F. (2002). Granulometric maps from high resolution satellite images. Image Analysis and Stereology, 21, 19-24.
  • 143. Moran E.F. (2010). Photogrammetric Engineering and Remote Sensing, 7(10), 1159-1168.
  • 144. Mróz M., Szumiło M. (2005). Metody i podejścia stosowane w integrującym przetwarzaniu obrazów teledetekcyjnych pozyskanych za pomocą różnych sensorów. Geodesia et Descripto Terrarum, 4(1), 17-28.
  • 145. Mura D.A., Benediktsson J.A., Bruzzone L. (2011). Self-dual Attribute Profiles for the Analysis of Remote Sensing Images. P. Soille M. Pesaresi, and G.K. Ouzounis (red.), ISMM 2011, Sprigner-Verlag, Berlin, Heidelberg, 320-330.
  • 146. Mura D.A., Benediktsson J.A., Waske B., Bruzzone L. (2010). Morphological Attribute Profiles for the Analysis of Very High Resolution Images. IEEE Transactions on Geoscience and Remote Sensing, 48(10), 3747-3762.
  • 147. Myint S.W., (2000). Image texture analysis with high-resolution multi-spectral image data using wavelet transforms. publikacja internetowa University Consortium for Geographic Information Systems (http://dusk2.geo.orst.edu/ucgis/web/oregon/papers/myint.htm). data dostępu: 1.06.2014.
  • 148. Navarro R.D.Jr., Magadia J.C., Paringit E.C. (2009). Estimating the Gauss-Markov Random Field Parameters for Remote Sensing Image Textures. TENCON 2009 - 2009 IEEE Region 10 Conference, 1-6.
  • 149. Neubert M., Herold H., Meinel G. (2006). Evaluation of remote sensing image segmentation quality - further results and concepts. ISPRS Archives - Volume XXXVI-4/C42.
  • 150. Neubert M., Herold H., Meinel G. (2008). Assessing image segmentation quality - concepts, methods and application. Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Blaschke, Th., Lang S., Hay G.J. (red.) Springer-Verlag, Berlin Heidelberg, 769-784.
  • 151. Newsam S. Bhagavathy S., Manjunath B.S. (2002). Modeling object classes aerial images using hidden Markov models, IEEE International Conference on Image Processing, 1, 860-863.
  • 152. Newsam S. Bhagavathy S., Manjunath B.S. (2003) Object localization using texture motifs and Markov random fields, IEEE International Conference on Image Processing, 2, 1049-1052.
  • 153. Niemeyer I., Marpu P.R., Nussbaum S. (2008). Change detection using object features. Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Blaschke, Th., Lang S., Hay G.J. (red.), Springer-Verlag, Berlin Heidelberg, 185-201.
  • 154. Nieniewski M. (1998). Morfologia matematyczna w przetwarzaniu obrazów, Akademicka Oficyna Wydawnicza PLJ, Warszawa, ss. 311.
  • 155. Nieniewski M. (2005). Segmentacja obrazów cyfrowych. Metody segmentacji wododziałowej. Akademicka Oficyna Wydawnicza EXIT, Warszawa, ss. 184.
  • 156. Olszewski R. (2003). Modelowanie kartograficzne z wykorzystaniem neurorozmytych automatów komórkowych. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 13A, 171-180.
  • 157. Olszewski R. (2009). Kartograficzne modelowanie rzeźby terenu metodami inteligencji obliczeniowej. Prace naukowe / Geodezja 46, Oficyna Wydawnicza Politechniki Warszawskiej, ss. 226.
  • 158. Oruc M., Marangoz A.M., Buyuksalih G. (2004).Comparison of pixel-based and object-oriented classification approaches using landsat-7 ETM spectral bands. ISPRS Archives - Volume XXXV Part B4, 1118-1122.
  • 159. Pasquariello G. (1992). Multitemporal Remote Sensing Data Classification Using Neural Network. ISPRS Archives - Volume XXIX Part B3, 922-929.
  • 160. Peddle D.R., and S.E. Franklin, 1989. High resolution satellite image texture for moderate relief terrain analysis, Proc. IGARSS 89, Vancouver B.C., 2, 653-654.
  • 161. Pesaresi M., Benediktsson J.A. (2001). A New Approach for the Morphological Segmentation of High-Resolution Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(2), 309-320.
  • 162. Platt R.V., Rapoza L. (2008). An evaluation of an object-oriented paradigm for land use/land cover classification. The Professional Geographer, 60(1), 87-100.
  • 163. Pluto-Kossakowska J. (2003). Analiza metod przetwarzania i interpretacji zdjęć satelitarnych SPOT z punktu widzenia potrzeb systemu informacji o glebach. Rozprawa doktorska, Politechnika Warszawska.
  • 164. Popławski W. (2014). Badanie przydatności map granulometrycznych do wyodrębniania terenów zabudowanych na zdjęciach lotniczych. Praca magisterska, Politechnika Warszawska.
  • 165. Preston C.J. (1974). Gibbs States on Countable Sets, Cambridge University Press.
  • 166. Pyka K. (2013). Mozaikowanie ortoobrazów z zastosowaniem transformacji falkowej. Wydawnictwa AGH, ss. 100.
  • 167. Pyka K., (2007). Zastosowanie transformacji falkowej do detekcji i usuwania szumów z danych rastrowych i pseudo-rastrowych. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 17,681-689.
  • 168. Pyka K. (2004a). Poszukiwanie falkowych miar potencjału informacyjnego obrazów jako wskaźników jakości wizualnych. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 14.
  • 169. Pyka K. (2004b). Transformacja falkowa jako efektywna metoda kompresji internetowych publikacji kartograficznych. Roczniki Geomatyki 2(2), 129-135.
  • 170. Pyka K. (2005). Falkowe wskaźniki zmian radiometrycznych zachodzących w procesie opracowania ortofotomapy. Rozprawy, Monografie AGH, Uczelniane Wydawnictwa Naukowo-Dydaktyczne, Kraków, ss. 94.
  • 171. Qiu F., Jensen J.R. (2004). Opening the black box of neural networks for remote sensing image classification. International Journal of Remote Sensing, 25(9), 1749-1768.
  • 172. Quattrochi D.A. N.S.N. Lam, H. Qiu, and Wei Zhao, 1997. Image Characterization and Modeling System (ICAMS): A Geographic Information System for the Characterization and Modeling of Multi-scale Remote Sensing Data, Scale in Remote Sensing and GIS (D.A. Quattrochi and M.F. Goodchild, editors), CRC Press, Boca Raton, Florida, 295-308.
  • 173. Raghu P.P., Poongodi R., Yegnanarayana B. (1995). A Combined Neural Network Approach for Texture Classification. Neural Networks, 8(6), 975-987.
  • 174. Raheja Lal J., Kumar S., Chaudhary A. (2013). Fabric defect detection based on GLCM and Gabor filter: A comparison. Optik - International Journal for Light and Electron Optics, 124(23), 6469-6474.
  • 175. Randen T., Husøy. J.H. (1999). Filtering for texture classification: a comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4), 291-310.
  • 176. Reulke R., Lippok A. (2008). Markov Random Fields (MRF) - Based Texture Segmentation for Road Detection. ISPRS Archives XXXVII Part B3b Chen J., Jiang J., Förstner W. (red.), 615-620.
  • 177. Richards J.A. (2013). Remote Sensing Digital Image Analysis. Fifth Edition, Springer, ss. 494.
  • 178. Robertson T.V. (1973). Extraction and Classification of Objects in Multispectral Images. LARS Symposia. Paper 21.
  • 179. Roli F., Serpico S.B. (1995). Land cover classification in remote-sensing images using structured neural networks. EARSel Advances in Remote Sensing, Satellite Technology and GIS for Mediterranean forest mapping and fire management, 4(3), 107-115.
  • 180. Ruiz L.A., Fdez-Sarria A., Recio J.A. (2004). Texture Feature Extraction for Classification of Remote Sensing Data Using Wavelet Decomposition: a Comparative Study. ISPRS Proceedings, Istambul 2004.
  • 181. Ruiz L.A., Fdez-sarria A., Recio J.A. (2004). Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comparative study. International Archives of Photogrammetry and Remote Sensing. Vol. XXXV.
  • 182. Ryan T. (1985). Image segmentation algorithms. Architectures and Algorithms for Digital Image Processing II, Proceedings of SPIE, 354, 172-178.
  • 183. Sandau K., Kurz H. (1997). Measuring fractal dimension and complexity - an alternative approach with an application. Journal of Microscopy, 186(2), 164-176.
  • 184. Sande van der, C.J., Jong de, S.M., Roo de, A.P.J. (2003). A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of Applied Earth Observation and Geoinformation 4, 217-229.
  • 185. Schiewe J. (2002). Segmentation of high-resolution remotely sensed data concepts, applications and problems. Proceedings of Symposium on Geospatial Theory, Processing and Applications, Ottawa 2002.
  • 186. Schowengerdt R.A. (1997). Remote Sensing. Models and Methods for age Processing. Second Edition. Academic Press, ss. 522.
  • 187. Schröder M., Rehrauer H., Seidel K., Datcu M. (1998). Spatial information retrieval from remote-sensing images. II. Gibbs-Markov random fields. IEEE Transactions on Geoscience and Remote Sensing, 36(5), 1446-1455.
  • 188. Sebastian B., Unnikrishnan A., Balakrishnan K. (2012). Grey Level Co-Occurrence Matrices: Generalisation and Some New Features. International Journal of Computer Science, Engineering and Information Technology, 2(2), 151-157.
  • 189. Serra J. (1982). Image Analysis and Mathematical Morphology, vol. 1, Academic Press, London, ss. 610.
  • 190. Sharma K.M.S., Sarkar A. (1998). A Modified Contextual Classification Technique for Remote Sensing Data. Photogrammetric Engineering & Remote Sensing, 64(4), 273-280.
  • 191. Sibson R. (1973). SLINK: an optimally efficient algorithm for the single-link cluster method. The Computer Journal (British Computer Society) 16(1), 30-34.
  • 192. Snow R.S., Mayer L. (1992). Fractals in Geomorphology, ss. 194.
  • 193. Soto A., Textures Classification Using Markov Random Fields. http://intrawww.ing.puc.cl/siding/public/ingcursos/cursos_pub/descarga.phtml?id_curso_ic=5034&id_archivo=190382, data dostępu: 02.07.2014.
  • 194. Spitzer F. (1971). Random fields and interacting particle systems. M.A.A. Summer Seminar Notes.
  • 195. Sternberg S.R. (1986). Grayscale Morphology. Computer Vision Graphics and Image Processing, 35(3), 333-355.
  • 196. Stumpf A., Kerle N. (2011). Object-oriented mapping of landslides using Random Forests, Remote Sensing of Environment, 115, 2564-2577.
  • 197. Sun W., Xu G., Gong P., Liang S. (2006). Fractal analysis of remotely sensed images: A review of methods and applications. International Journal of Remote Sensing, 27( 22), 4963-4990.
  • 198. Swain P.H., Davis S.M. (1978). Remote Sensing; The Quantitative Approach. Mcgraw-Hill College, ss. 396.
  • 199. Szeszko A. (2014). Analiza rozwoju fragmentu zabudowy miasta Torunia na podstawie zdjęć z Landsat 7 z wykorzystaniem map granulometrycznych. Praca inżynierska, Politechnika Warszawska.
  • 200. Tadeusiewicz R. (1993). Sieci neuronowe. Akademicka Oficyna Wydawnicza, Warszawa, ss. 130.
  • 201. Thomas, V., Treitz P., Jelinski D., Miller J., Lafteur P., McCaughey J.H. (2002). Swain, Philip H., and Shirley M. Davis. 1978. Remote Sensing: The Quantitative Approach. Remote Sensing of Environment, 84, 83-99.
  • 202. Tsai I.W., Tseng D.C. (1997). Segmentation of multispectral remote-sensing images based on Markov random fields. Geoscience an Remote Sensing, 1997. IGARSS ‘97. Remote Sensing - A Scientific Vision for Sustainable Development, IEEE International, 1, 264-266.
  • 203. Unser M. (1995). Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing, 4(11), 549-1560.
  • 204. Valero S., Chanussot J., Benediktsson J.A., Talbot H., Waske B. (2010). Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images. Pattern Recognition Letters, 31(10), 1120-1127.
  • 205. Van Coillie F.M.B., Verbeke L.P.C., De Wulf R.R. (2006). Semi-Automated Forest Stand Delineation Using Wavelet-Based Segmentation of Very High Resolution Optical Imagery in Flanders, Belgium. [w:] Lang S., Blaschke T., Schöpfer E., (red.), 1st International Conference on Object-based Image Analysis (OBIA 2006) Workshop proceedings, Salzburg.
  • 206. Vardhan A.S., Sagar D.B.S., Rajesh N., Rajashekara H.M. (2013). Automatic Detection of Orientation of Mapped Units via Directional Granulometric Analysis. IEEE Geoscience and Remote Sensing Letters, 10(6), 1449-1453.
  • 207. Vincent L. (1992). Morphological Area Opening and Closings for Greyscale Images, Proceedings of Shape in Picture'92 - NATO Workshop, Springer Verlag.
  • 208. Vincent L. (1996). Opening Trees And Local Granulometries. In: Proc. Mathematical Morphology and Its Applications to Signal Processing, Georgia, USA, 273-280.
  • 209. Volotão de Sá C.F., Gelelete C. (2007). Linear features detection in CCD/CBERS-2 image using neural network. Anais XIII Simposio Brasilleiro de Sensoriamento Remota, INPE, 1205-1210.
  • 210. Wald L. (1999). Definition and terms of reference in data fusion. International Archives of Photogrammetry and Remote Sensing, 32 (7-4-3), W6, 2-6.
  • 211. Walker J.S., Briggs J.M., (2007). An object-oriented approach to urban rarest mapping in Phoenix. Photogrammetric Engineering & Remote Sensing, 73(5), 577-583.
  • 212. Walter, V. (2004). Object-based classification of remote sensing data for change detection. Photogrammetry and Remote Sensing, 58, 225-238.
  • 213. Walton J.T. (2008). Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression. Photogrammetric Engineering & Remote Sensing, 74(10), 1213-1222.
  • 214. Wang C.K. (2012). Exploring Weak and Overlapped Returns of a Lidar Waveform with a Wavelet-Based Echo Detector. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25, 529-534.
  • 215. Wang L., Liu J. (1999). Texture classification using multiresolution Markov random field models. Pattern Recognition Letters, 20(2), 171-182.
  • 216. Wawrzaszek A., Krupinski M., Drzewiecki W., Aleksandrowicz S. (2014). Influence of satellite image filtration on fractal and multifractal features in the context of land cover classification. Photogrammetrie, Fernerkundung, Geoinformation 2, 101-115.
  • 217. Werbos P.J. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvard University, 1974.
  • 218. Werbos P.J. (1990). Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10), 1550-1560.
  • 219. Werbos P.J. (1994) The Roots of Backpropagation; From Ordered Derivatives to Neural Networks and Political Forecasting. Wiley, New York, ss. 319.
  • 220. Woodcock C., and V.J. Harward, 1992. Nested-hierarchical scene models and image segmentation, International Journal of Remote Sensing, 13, 3167-3187.
  • 221. Woodcock C.E., Strahler A.H. Jupp D.L.B. (1988a). The use of Variograms in Remote Sensing: I. Scene Models and Simulated Images, Remote Sensing of Environment, 25, 323-348.
  • 222. Woodcock C.E., Strahler A.H., Jupp D.L.B. (1988b). The use of Variograms in Remote Sensing: II. Real Digital Images, Remote Sensing of Environment, 25, 349-379.
  • 223. Wu H., Sun Y., Shi W., Chen X., Fu D. (2013). Examining the Satellite-Detected Urban Land Use Spatial Patterns Using Multidimensional Fractal Dimension Indices. Remote Sensing, 5, 5152-5172.
  • 224. Yin S., Wang W. (2006) Denoising lidar signal by combining wavelet improved threshold with wavelet domain spatial filtering. Chinese Optics Letters, 4(12), 694-696.
  • 225. Zhang X., Pengfeng X., Xuezhi F. (2012). An Unsupervised Evaluation Method for Remotely Sensed Imagery Segmentation. IEEE Geoscience and Remote Sensing Letters, 9(2), 156-160.
  • 226. Zhao Y., Zhang L., Li P., Huang B. (2007). Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features. IEEE Transactions on Geoscience and Remote Sensing, 45(5), 1458-1468.
  • 227. Zhu C., Yang X. (1998). Study of remote sensing image texture analysis and classification using wavelet. International Journal of Remote Sensing, 13, 3167-3187.
  • 228. Zhu H., Chen H. (2009). A quantitative evaluation of image segmentation quality. Proceedings of ASPRS 2009 Annual Conference.
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