Remote Sensing Classification Using Fractal Dimensions over a Subtropical Hilly Region
Subtropical hilly regions in China have various and complex surface features, which are difficult to identify in remote-sensing images. A new algorithm for calculating the fractal dimension of a single pixel of a remote-sensing image is presented. The fractal dimension data for a scene captured in an ETM+ (i.e., Enhanced Thematic Mapper Plus) image were used to compose a new image together with the second and fourth bands of the ETM+ image. Using the new image, the land-use/land-cover types and forest categories in the region were identified using a maximum likelihood classification. The accuracy assessment of the classification gave an overall accuracy of 80.69 percent and a Khat value of 0.78. The proposed method is a little more accurate than the method that does not use fractal dimension data, especially for identification of different types of vegetation in the region.
No References for this article.
No Supplementary Data.
No Article Media
Document Type: Research Article
Publication date: January 1, 2011
More about this publication?
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
- Editorial Board
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Membership Information
- Information for Advertisers
- Terms & Conditions
- Ingenta Connect is not responsible for the content or availability of external websites