Object-oriented classification of very high-resolution remote sensing imagery based on improved CSC and SVM

We present a new object-oriented land cover classification method integrating raster analysis and vector analysis, which adopted improved Color Structure Code (CSC) for segmentation and Support Vector Machine (SVM) for classification using Very High Resolution (VHR) QuickBird data. It synthesized the advantage of digital image processing, Geographical Information System (GIS) (vector-based feature selection) and Data Mining (intelligent SVM classification) to interpret image from pixels to segments and then to thematic information. Compared with the pixelbased SVM classification in ENVI 4.3, both of the accuracy of land cover classification by the proposed method and the computational performance for classification were improved. Moreover, the land cover classification map can update GIS database in a quick and convenient way.

[1]  P. Gong,et al.  The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. , 1990 .

[2]  Roland Wilson,et al.  Unsupervised image segmentation combining region and boundary estimation , 2000, Image Vis. Comput..

[3]  S. Mitra,et al.  Color Image Segmentation : A State-ofthe-Art Survey , 2022 .

[4]  Giles M. Foody,et al.  Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data , 1996 .

[5]  Lorenzo Bruzzone,et al.  A multilevel hierarchical approach to classification of high spatial resolution images with support vector machines , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[6]  Kanellopoulos Ioannis,et al.  Integration of Neural and Statistical Approaches in Spatial-Data Classification , 1995 .

[7]  Paul M. Mather,et al.  Assessment of the effectiveness of support vector machines for hyperspectral data , 2004, Future Gener. Comput. Syst..

[8]  L. Moeller-Jensen,et al.  Knowledge-based classification of an urban area using texture and context information in Landsat-TM imagery , 1990 .

[9]  Jeong Chang Seong,et al.  Fuzzy Image Classification for Continental-Scale Multitemporal NDVI Series Images Using Invariant Pixels and an Image Stratification Method , 2001 .

[10]  Zhang Xuegong,et al.  INTRODUCTION TO STATISTICAL LEARNING THEORY AND SUPPORT VECTOR MACHINES , 2000 .

[11]  Graeme G. Wilkinson,et al.  Results and implications of a study of fifteen years of satellite image classification experiments , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Lutz Priese,et al.  Fast and Robust Segmentation of Natural Color Scenes , 1998, ACCV.

[13]  Giovanni Cuozzo,et al.  A method based on tree-structured Markov random field for forest area classification , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.