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While it is well-known that texture can be used to classify very high resolution (VHR) data, the limits of its applicability have not been unequivocally specified. This study examines whether it is possible to divide satellite images into two classes associated with “low” and “high” texture values in the initial stage of processing VHR images. This approach can be effectively used in object-oriented classification. Based on the panchromatic channel of KOMPSAT-2 images from five areas of Europe, datasets with down-sampled pixel resolutions of 1, 2, 4, 8, and 16 m were prepared. These images were processed using different texture analysis techniques in order to discriminate between basic land cover classes. Results were assessed using the normalized feature space distance expressed by the Jeffries–Matusita distance. The best results were observed for images with the highest resolution processed by the Laplacian filter. Our research shows that a classification approach based on the idea of “low” and “high” textures can be effectively applied to panchromatic data with a resolution of 8 m or higher.
Wydawca
Czasopismo
Rocznik
Tom
Strony
547--567
Opis fizyczny
Bibliogr. 29 poz., rys., tab., wykr.
Twórcy
autor
- Space Research Centre, Polish Academy of Sciences, Warszawa, Poland
autor
- Space Research Centre, Polish Academy of Sciences, Warszawa, Poland
autor
- Space Research Centre, Polish Academy of Sciences, Warszawa, Poland
Bibliografia
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- [2] Blaschke, T. (2010), Object based image analysis for remote sensing, ISPRS J. Photogramm. Remote Sens. 65, 1, 2-16, DOI: 10.1016/j.isprsjprs.2009. 06.004.
- [3] De Kok, R. (2012), Spectral difference in the image domain for large neighborhoods, a GEOBIA pre-processing step for high resolution imagery, Remote Sens. 4, 8, 2294-2313, DOI: 10.3390/rs4082294.
- [4] De Kok, R., and P. Wezyk (2008), Principles of full autonomy in image interpretation. The basic architectural design for a sequential process with image objects. In: Th. Blaschke, S. Lang, and G.J. Hay (eds.), Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Lecture Notes in Geoinformation and Cartography, Springer, Berlin Heidelberg, 697-710, DOI: 10.1007/978-3-540-77058-9_38.
- [5] De Martinao, M., F. Causa, and S.B. Serpico (2003), Classification of optical high resolution images in urban environment using spectral and textural information. In: Proc. IEEE Int. Symp. Geoscience and Remote Sensing IGARSS’03, 21-25 July 2003, Toulouse, France, DOI: 10.1109/IGARSS.2003.1293811.
- [6] Eckert, S. (2012), Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data, Remote Sens. 4, 4, 810-829, DOI: 10.3390/rs4040810.
- [7] eCognition Developer (2011), eCognition Developer, Reference book, Trimble documentation, Munich, Germany.
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- [9] Hall-Beyer, M. (2000), GLCM texture: a tutorial, Department of Geography, University of Calgary, Calgary, Canada, http://www.fp.ucalgary.ca/mhallbey/tutorial.htm.
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- [12] Hofmann, T., J. Puzicha, and J.M. Buhmann (1998), Unsupervised texture segmentation in a deterministic annealing framework, IEEE Trans. Pattern Anal. Mach. Intellig. 20, 8, 803-818, DOI: 10.1109/34.709593.
- [13] Hu, X., C.V. Tao, and B. Prenzel (2005), Automatic segmentation of high-resolution satellite imagery by integrating texture, intensity, and colour features, Photogramm Eng. Rem. Sens. 71, 12, 1399-1406, DOI: 10.14358/PERS. 71.12.1399.
- [14] Intergraph (1999), Image Analyst User’s Guide for Windows, Intergraph Corporation, Huntsville, USA.
- [15] Jain, A.K. (1989), Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cliffs, 569 pp.
- [16] Jensen, J.R. (1996), Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd ed., Prentice Hall, Upper Saddle River, 316 pp.
- [17] Kit, O., M. Lüdeke, and D. Reckien (2012), Texture-based identification of urban slums in Hyderabad, India using remote sensing data, Appl. Geogr. 32, 2, 660-667, DOI:10.1016/j.apgeog.2011.07.016.
- [18] Lewinski, S., and Z. Bochenek (2009), Rule-based classification of SPOT imagery using object-oriented approach for detailed land cover mapping. In: Proc. 28th EARSeL Symp. “Remote Sensing for a Changing Europe”, 2-5 June 2008, Istanbul, Turkey.
- [19] Lewinski, S., Z. Bochenek, and K. Turlej (2014), Application of an object-oriented method for classification of VHR satellite images using rule-based approach and texture measures. In: I. Manakos and M. Braun (eds.), Land Use and Land Cover Mapping in Europe: Practices and Trends, Remote Sensing and Digital Image Processing, Vol. 18, 193-201, Springer Science + Business Media, Dordrecht, DOI: 10.1007/978-94-007-7969-3_12.
- [20] Morales, D.I., M. Moctezuma, and F. Parmiggiani (2003), Urban and non urban area classification by texture characteristics and data fusion. In: Proc. IEEE Int. Symp. Geoscience and Remote Sensing IGARSS’03, 21-25 July 2003, Toulouse, France, 3504-3506, DOI: 10.1109/IGARSS.2003.1294835.
- [21] Murray, H., A. Lucieer, and R. Williams (2010), Texture-based classification of subAntarctic vegetation communities on Heard Island, Int. J. Appl. Earth Observ. Geoinform. 12, 3, 138-149, DOI: 10.1016/j.jag.2010.01.006.
- [22] Nussbaum, S., I. Niemeyer, and M.J. Canty (2006), SEaTH – A new tool for automated feature extraction in the context of object-based image analysis. In: Proc. 1st Int. Conf. on Object-based Image Analysis (OBIA), 4-5 July 2006, Salzburg, Austria.
- [23] Puissant, A., J. Hirsch, and C. Weber (2005), The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery, Int. J. Remote Sens. 26, 4, 733-745, DOI: 10.1080/01431160512331316838.
- [24] Ryherd, S., and C. Woodcock (1996), Combining spectral and texture data in the segmentation of remotely sensed images, Photogramm. Eng. Remote Sens. 62, 2, 181-194.
- [25] Su, W., J. Li, Y. Chen, Z. Liu, J. Zhang, T.M. Low, I. Suppiah, and S.A.M. Hashim (2008), Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery, Int. J. Remote Sens. 29, 11, 3105-3117, DOI: 10.1080/01431160701469016.
- [26] Thomas, V., P. Treitz, D. Jelinski, J. Miller, P. Lafleur, and J.H. McCaughey (2003), Image classification of a northern peatland complex using spectral and plant community data, Remote Sens Environ. 84, 1, 83-99, DOI: 10.1016/S0034-4257(02)00099-8.
- [27] Tuceryan, M., and A.K. Jain (1999), Texture analysis. In: C.H. Chen, L.F. Pau, and P.S.P. Wang (eds.), The Handbook of Pattern Recognition and Computer Vision, 2nd ed., World Scientific Publ. Co., Singapore, 207-248.
- [28] Wang, Y.W., Y.F. Wang, Y. Xue, and W. Gao (2003), A new algorithm for remotely sensed image texture classification and segmentation. In: Proc. IEEE Int. Symp. Geoscience and Remote Sensing IGARSS’03, 21-25 July 2003, Toulouse, France, 3534-3536, DOI: 10.1109/IGARSS.2003.1294845.
- [29] Wezyk, P., and R. De Kok (2006), Automatic mapping of the dynamics of forest succession on abandoned parcels in south Poland. In: J. Strobl, T. Blaschke, and G. Griesebner (eds.), Angewandte Geoinformatik 2005, Wichman Verlag, Heidelberg, 774-779 (in German).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7fe53329-d096-49dc-85f9-4e2819ec6aed