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.
Original language | English |
---|---|
Article number | 4686022 |
Pages (from-to) | 3804-3814 |
Number of pages | 11 |
Journal | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 46 |
Issue number | 11 |
DOIs | |
Publication status | Published - Nov 2008 |
Bibliographical note
Funding Information:Manuscript received November 11, 2007; revised February 14, 2008. Current version published October 30, 2008. This work was supported in part by the Research Fund of the University of Iceland and in part by the Jules Verne Program of the French and Icelandic governments (PAI EGIDE).
Funding Information:
Airborne data from the ROSIS-3 (Reflective Optics System Imaging Spectrometer) optical sensor are used for the experiments. The flight over the city of Pavia, Italy, was operated by the Deutschen Zentrum fur Luft-und Raumfahrt (DLR, the German Aerospace Agency) in the framework of the HySens project, and managed and sponsored by the European Union. According to specifications, the number of bands of the ROSIS-3 sensor is 115 with a spectral coverage ranging from 0.43 to 0.86 μm. The data have been atmospherically corrected
Other keywords
- Data fusion
- Extended morphological profile (EMP)
- Feature extraction (FE)
- High spatial resolution
- Hyperspectral data
- Support vector machines (SVMs)