Kernel methods for HyMap imagery knowledge discovery

Gustavo Camps-Valls; Luis Gomez-Chova; Javier Calpe-Maravilla; Emilio Soria-Olivas; Jose D. Martin-Guerrero; Jose Moreno

doi:10.1117/12.510719

5 February 2004 Kernel methods for HyMap imagery knowledge discovery

Gustavo Camps-Valls, Luis Gomez-Chova, Javier Calpe-Maravilla, Emilio Soria-Olivas, Jose D. Martin-Guerrero, Jose Moreno

Author Affiliations +

Proceedings Volume 5238, Image and Signal Processing for Remote Sensing IX; (2004) https://doi.org/10.1117/12.510719
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

In this paper, we propose a kernel-based approach for hyperspectral knowledge discovery, which is defined as a process that involves three steps: pre-processing, modeling and analysis of the classifier. Firstly, we select the most representative bands analyzing the surrogate and main splits of a Classification And Regression Trees (CART) approach. This yields three datasets with different reduced input dimensionality (6, 3 and 2 bands, respectively) along with the original one (128 bands). Secondly, we develop several crop cover classifiers for each of them. We use Support Vector Machines (SVM) and analyze its performance in terms of efficiency and robustness, as compared to multilayer perceptrons (MLP) and radial basis functions (RBF) neural networks. Suitability to real-time working conditions, whenever a preprocessing stage is not possible, is evaluated by considering models with and without the CART-based feature selection stage. Finally, we analyze the support vectors distribution in the input space and through Principal Component Analysis (PCA) in order to gain knowledge about the problem. Several conclusions are drawn: (1) SVM yield better outcomes than neural networks; (2) training neural models is unfeasible when working with high dimensional spaces; (3) SVM perform similarly in the four classification scenarios, which indicates that noisy bands are successfully detected and (4) relevant bands for the classification are identified.

Citation Download Citation

Gustavo Camps-Valls, Luis Gomez-Chova, Javier Calpe-Maravilla, Emilio Soria-Olivas, Jose D. Martin-Guerrero, and Jose Moreno "Kernel methods for HyMap imagery knowledge discovery", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); https://doi.org/10.1117/12.510719

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