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Conference Paper
Land-cover Supervised Classification using User-oriented Feature Database
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- Authors
- Geun-Won Yoon, Jeong-Ho Park, Kyoung-Ho Choi
- Issue Date
- 2004-09
- Citation
- International Geoscience and Remote Sensing Symposium (IGARSS) 2004, pp.2724-2726
- Language
- English
- Type
- Conference Paper
- Abstract
- In order to utilize remote sensed images effectively, a lot of image classification methods are suggested for many years. But the accuracy of traditional methods based on pixel-based classification is not high in general. And, in case of supervised classification, users should select training data sets within the image that are representative of the land-cover classes of interest. But users feel inconvenience to extract training data sets for image classification. In this paper, object oriented classification of Landsat images using feature database is studied in consideration of user’s convenience and classification accuracy. Object oriented image classification, currently a new classification concept, allows the integration of a spectral value, shape and texture and creates image objects. According to classification classes, objects statistics such as mean value, standard deviation and tasseled cap transformation component were constructed as feature database. The feature of seven classes (Rural, Forest, Grass, Agriculture, Wetland, Barren, Water) was constructed in this study, it will be served in a network to user for image classification training data sets. Proposed method has higher classification accuracy than that of traditional pixel-based supervised classification and gives convenient environment to users.