Techniques based on Support Vector Machines for cloud detection on QuickBird satellite imagery

Purpose of this work is the study of cloud detection techniques. This work identifies the cloud cover of optical images acquired by the QuickBird satellite, comparing these with others of the same area, acquired by Landsat 7 in which there are no clouds. The images are combined using an early fusion technique [1]. The tool exploits the neighborhood model [2] for increasing the amount of information for the training set and the Singular Value Decomposition for carrying out the feature extraction [3]. In order to introduce these structures into thematic classification tasks by SVMs it was necessary develop a tree kernel function based on tree kernel function defined in SVM-LightTK. The aim of the tree kernel function is evaluate the similarity level between a generic couples of tree structures. In this paper we report the results obtained comparing the performance of different approaches in cloud classification problem. The final purpose is the production of cloud cover maps. Throughout such different experimental setups we measured the capabilities of each algorithm under different points of view. First of all, we considered the classification accuracy by computing traditional parameter such as overall accuracy. A second analysis regarded the efforts that are required in the design of optimal algorithms. Indeed, these techniques are characterized by different parameters that have to be appropriately tuned in order to obtain the best performance. Finally the robustness of the techniques has been also considered. In particular the classification accuracy has been evaluated also for images not considered in the training phase.

[1]  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).

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

[3]  Roberto Basili,et al.  A Comparative Analysis of Kernel-Based Methods for the Classification of Land Cover Maps in Satellite Imagery , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[5]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[6]  Ramachandra Raghavendra,et al.  Texture Based Approach for Cloud Classification Using SVM , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[7]  Gene H. Golub,et al.  Calculating the singular values and pseudo-inverse of a matrix , 2007, Milestones in Matrix Computation.

[8]  Mahmood R. Azimi-Sadjadi,et al.  A study of cloud classification with neural networks using spectral and textural features , 1999, IEEE Trans. Neural Networks.

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

[10]  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).

[11]  Giles M. Foody,et al.  Multiclass and Binary SVM Classification: Implications for Training and Classification Users , 2008, IEEE Geoscience and Remote Sensing Letters.