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Land-use classification using multitemporal ers-1, radarsat and jers sar-images.
| Content Provider | CiteSeerX |
|---|---|
| Author | Törmä, Markus Koskinen, Jarkko |
| Abstract | Land-use classification was performed by using a set of ERS-1, JERS- and Radarsat images. Classes were water, forests (with subclasses according to stem volume), agricultural field, mire and urban area. Median filtering was used for speckle reduction and principal component analysis for feature extraction. Spectral classification was performed by using self-organizing feature map and learning vector quantization. Contextual classification was performed as postprocessing step. The overall accuracy of the spectral classification was 86.4% and the best contextual classification 89.8%. 1. INTRODUCTION Many governmental institutions have a continuing requirement to form and implement laws and policies that involve existing or future land-use. Optical aerial and satellite images have been used long time to produce information about the current land-use. Unfortunately weather conditions limit the use of optical data. For example, here in Finland summer is usually quite cloudy, there are usua... |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Land-use Classification Using Multitemporal Ers-1 Jers Sar-images Contextual Classification Spectral Classification Overall Accuracy Postprocessing Step Feature Extraction Self-organizing Feature Map Principal Component Analysis Long Time Future Land-use Satellite Image Agricultural Field Weather Condition Land-use Classification Speckle Reduction Finland Summer Optical Data Stem Volume Vector Quantization Median Filtering Introduction Many Governmental Institution Radarsat Image Current Land-use Urban Area |
| Content Type | Text |
