Article

Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles

Signal & Image Dept., Grenoble Inst. of Technol., Grenoble
IEEE Transactions on Geoscience and Remote Sensing (Impact Factor: 3.51). 12/2008; 46(11):3804 - 3814. DOI: 10.1109/TGRS.2008.922034
Source: IEEE Xplore

ABSTRACT

A method is proposed for the classification of urban hyperspectral data with high spatial resolution. The approach is an extension of previous approaches and uses both the spatial and spectral information for classification. One previous approach is based on using several principal components (PCs) from the hyperspectral data and building several morphological profiles (MPs). These profiles can be used all together in one extended MP. A shortcoming of that approach is that it was primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. Similarly, the commonly used pixelwise classification of hyperspectral data is solely based on the spectral content and lacks information on the structure of the features in the image. The proposed method overcomes these problems and is based on the fusion of the morphological information and the original hyperspectral data, i.e., the two vectors of attributes are concatenated into one feature vector. After a reduction of the dimensionality, the final classification is achieved by using a support vector machine classifier. The proposed approach is tested in experiments on ROSIS data from urban areas. Significant improvements are achieved in terms of accuracies when compared to results obtained for approaches based on the use of MPs based on PCs only and conventional spectral classification. For instance, with one data set, the overall accuracy is increased from 79% to 83% without any feature reduction and to 87% with feature reduction. The proposed approach also shows excellent results with a limited training set.

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    • "However, single feature representation methods are unable to describe all relevant information, thus a multiple feature fusion method was developed. M. Fauvel used morphological properties to describe spatial information in order to combine spectrum characteristics [14]. This study overcomes the disadvantages of single feature classification and greatly improves the classification precision of the pixel scale. "

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