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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fang Huang Yina Qi |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Urban and Environmental Sciences, Northeast Normal University, Changchun, China (Fang Huang; Yina Qi) |
| Abstract | Land use/cover is the most prominent landscape sign of the earth surface system. Remote sensing technology provides the land use/cover information quickly and precisely. With the recent high-resolution commercial satellites successfully launched, the image may provide abundant geometrical and spatial information, including texture, structure and shape of ground target and so on. Traditional pixel-based classification approach is greatly limited and it will result in low precision of classification. In this study, taking Changbai Natural Reserve in northeastern China as an example, object-oriented approach was used to extract land cover classes based on eCognition. A model was proposed for the analysis of the most suitable scale of image segmentation. The experiment result shows that object-oriented approach has a great deal of advantage such as high efficiency, high precision, and more impersonality and so on. The precision of object-oriented approach under the most suitable segmentation scale was 87.25%, superior to the traditional pixel-based approach. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 288057 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424473014 |
| e-ISBN | 9781424473038 |
| DOI | 10.1109/GEOINFORMATICS.2010.5567786 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Accuracy Image resolution Changbai Natural Reserve Shape Segmentation scale IKONOS image Object-oriented image analysis Data mining Land cover Remote sensing Pixel |
| Content Type | Text |
| Resource Type | Article |
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