Application of an Object-Oriented Method for Classification of VHR Satellite Images Using a Rule-Based Approach and Texture Measures

Abstract

Since the launch of commercial software for object-oriented data analysis numerous research and application works were undertaken, in order to apply this concept and elaborate semi-automatic methods for land cover classification based on satellite images. The research works were concentrated on two main aspects of object-oriented approaches: multi-resolution segmentation to adjust objects to terrain elements in an optimal way and on classification methods, exploiting comprehensively spectral, spatial and textural features of image objects, as well as their mutual relationships. Applications range from studies using multi-resolution satellite data (Whiteside T, A multi-scale object-oriented approach to the classification of multi-sensor imagery for mapping land cover in the top end. Proceedings of NARGIS 2005 – application in tropical spatial science. 4th–7th July 2005 Charles Darwin University, Darwin, NT, Australia. http://www.ecognition.com/sites/default/files/272_18.1_20whiteside__20tim.pdf, 2005) to very high-resolution images (QuickBird, Ikonos), which enabled more effective analysis of texture and shape features (Wei W, Chen X, Ma A, Object-oriented information extraction and application in high-resolution remote sensing image. Proceedings of the IGARSS 2005 symposium. Seoul, Korea. July 25–29, 2005, pp 3803–3806, 2005; Kressler FP, Steinnocher K, Kim YS, Enhanced semi-automatic image classification of high-resolution data. Proceedings of the IGARSS 2005 symposium. Seoul, Korea. July 25–29, 2005. http://www.ecognition.com/sites/default/files/240_igarsskressler2.pdf, 2005; de Kok R, Wężyk P, Principles of full autonomy in image interpretation. The basic architectural design for a sequential process with image objects. In: Blaschke Th, Lang S, Hay GJ (eds) Object-based image analysis. Series: lecture notes in geoinformation and cartography. Springer, Berlin/Heidelberg, ISSN: 1863–2246, pp 697–710, 2008; de Kok R, Wezyk P, Weidenbach M, The role of edge objects in full autonomous image interpretation. Proceedings of GEOBIA 2008 – pixels, objects, intelligence, GEOgraphic object based image analysis for the 21st century, Calgary, Alberta, Canada. http://www.isprs.org/proceedings/XXXVIII/4-C1/Sessions/Session1/6717_DeKok_Proc_pap.pdf, 2008). A common classification approach was based on applying training areas for particular land cover classes and a Standard Nearest Neighbour Classifier to assign objects to land cover categories (Yuan F, Bauer ME, Mapping impervious surface area using high resolution imagery: a comparison of object-based and per pixel classification. Proceedings of ASPRS 2006 annual conference, Reno, Nevada; May 1–5, 2006. http://www.ecognition.com/sites/default/files/184_asprs2006_0178.pdf, 2006; Hajek F, Object-oriented classification of remote sensing data for the identification of tree species composition. Proceedings of ForestSat 2005 conference, May 31–June 3, 2005, Boras, Sweden. http://www.ecognition.com/sites/default/files/229_forestsat2005_20__20filip_20hajek.pdf, 2005; Elmqvist B, Ardo J, Olsson L, Int J Remote Sens 29(24):7129–7140, 2008). Alternative approaches for the classification process comprise the use of parametric values of spectral and texture type as well as hierarchical classification workflows, based on a decision tree method (Lewinski St, Bochenek Z, Rule-based classification of SPOT imagery using object-oriented approach for detailed land cover mapping. Proceedings of 28th EARSeL symposium “Remote sensing for a changing Europe”, Istanbul, Turkey, 2–5 June 2008, pp 197–204, 2008; Lucas R, Rowlands A, Brown A, Keyworth S, Bunting P, ISPRS J Photogramm Remote Sens 62(3):165–185, 2007; Su W, Li J, Chen Y, Liu Z, Zhang J, Low TM, Suppiah I, Hashim SAM, Int J Remote Sens 29(1):3105–3117, 2008). The presented work on an object based classification approach emerged from the needs formulated within the Geoland 2 SATChMo Core Mapping Service.