Classification of Multispectral High-Resolution Satellite Imagery Using LIDAR Elevation Data

This paper studies the influence of airborne LIDAR elevation data on the classification of multispectral SPOT5 imagery over a semi-urban area; to do this, multispectral and LIDAR elevation data are integrated in a single imagery file composed of independent multiple bands. The Support Vector Machine is used to classify the imagery. A scheme of five classes was chosen; ground truth samples were then collected in two sets, one for training the classifier and the other for checking its quality after classification. The results show that the integration of LIDAR elevation data improves the classification of multispectral bands; the assessment and comparison of the classification results have been carried out using complete confusion matrices. Improvements are evident in classes with similar spectral characteristics but for which altitude is a relevant discrimination factor. An overall improvement of 28.3% was obtained, when LIDAR was included.

[1]  E. Bork,et al.  Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis , 2007 .

[2]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[3]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[4]  José A. Malpica,et al.  Classification of High Resolution Satellite Images Using Texture from the Panchromatic Band , 2007, ISVC.

[5]  Klaus Steinnocher,et al.  Object-oriented classification of orthophotos to support update of spatial databases , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[6]  John Trinder,et al.  Building Detection Using LIDAR Data and Multispectral Images , 2003, DICTA.

[7]  Michael I. Posner,et al.  Cognition (2nd ed.). , 1987 .

[8]  Lucien Wald,et al.  Data Fusion. Definitions and Architectures - Fusion of Images of Different Spatial Resolutions , 2002 .

[9]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[10]  Simon D. Jones,et al.  AUTOMATIC CLASSIFICATION OF LAND COVER FEATURES WITH HIGH RESOLUTION IMAGERY AND LIDAR DATA: AN OBJECT-ORIENTED APPROACH , 2005 .

[11]  J. Shan,et al.  Combining Lidar Elevation Data and IKONOS Multispectral Imagery for Coastal Classification Mapping , 2003 .

[12]  M.R. Azimi-Sadjadi,et al.  Cloud classification using support vector machines , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[13]  G. Mercier,et al.  Support vector machines for hyperspectral image classification with spectral-based kernels , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  Bo Liu,et al.  A Comparision of Multiclass Support Vector Machine Algorithms , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[16]  Giles M. Foody,et al.  A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.