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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hongsheng Zhang Hui Lin Yu Li |
| Copyright Year | 2004 |
| Abstract | Land use/land cover (LULC) classification using optical and synthetic aperture radar (SAR) remote sensing images is becoming increasingly important to produce more accurate LULC products. As an important step, feature normalization techniques have been studied by the areas of pattern recognition. Nevertheless, because of the totally different imaging mechanisms of optical and SAR sensors, most of the existing normalization approaches are not suitable for optical and SAR data fusion. Moreover, whether normalization is a significant step remains unclear regarding optical and SAR fusion. Taking the Satellite Pour l'Observation de la Terre (SPOT-5) and the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band SAR (PALSAR) (HH and HV polarizations) as the optical and SAR data, this letter aims to evaluate the impact of feature normalization. Experimental results indicated that feature normalization is not necessarily significant depending on fusion methods. For instance, distribution-dependent classifiers (e.g., a maximum likelihood classifier) are independent of feature normalization; thus, it has no impact on the results when using these classifiers. Moreover, advanced classifiers (e.g., a support vector machine) with built-in normalization are also not influenced by feature normalization. In contrast, a minimum distance classifier and an artificial neural network (ANN) depend on the input values of optical and SAR features and thus can be influenced by feature normalization. However, our experiments showed a fluctuation in classification accuracy using an ANN with normalized features. Therefore, more experiments are required to investigate the optimal normalization approaches for the optical and SAR images when using an ANN as the fusion method. |
| Sponsorship | IEEE Geoscience and Remote Sensing Society |
| Starting Page | 1061 |
| Ending Page | 1065 |
| Page Count | 5 |
| File Size | 944636 |
| File Format | |
| ISSN | 1545598X |
| Volume Number | 12 |
| Issue Number | 5 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Synthetic aperture radar Optical imaging Remote sensing Optical sensors Accuracy Adaptive optics Feature extraction polarimetric SAR Land use/land cover (LULC) normalization optical and synthetic aperture radar (SAR) data fusion |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology |
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