SATELLITE IMAGES CLASSIFICATION BASED FRACTAL FEATURES | Zendy

Laith A. Al Ani | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

SATELLITE IMAGES CLASSIFICATION BASED FRACTAL FEATURES

Author(s) -

Laith A. Al Ani

Publication year - 2007

Publication title -

journal of al-nahrain university-science

Language(s) - English

Resource type - Journals

eISSN - 2519-0881

pISSN - 1814-5922

DOI - 10.22401/jnus.10.1.14

Subject(s) - lacunarity , fractal , fractal dimension , pattern recognition (psychology) , artificial intelligence , box counting , segmentation , fractal analysis , contextual image classification , classifier (uml) , block (permutation group theory) , computer science , mathematics , image (mathematics) , geometry , mathematical analysis

In this paper, a TM-multi-spectral satellite images is adopted in a purpose of supervised classification. The traditional method of the segmentation namely Quad tree is applied as pre processing step. For each segmented block, the fractal features (fractal dimension and lacunarity)s are determined to be used as a maximum likelihood classifier. The results showed that the fractal dimension has not certainly able to classify the segmented blocks while the lacunarity gave good classification results. In general, the fractal geometry was found an efficient parameter for describing the image. The results show that the over all classification accuracy is 85.5%.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research