Estimating the natural number of classes on hierarchically clustered multi-spectral images

• Type: Article in International Scientific Journal
• Date: 2005

Authors

marcal, ars (Author) FCUP; borges, js (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: INTERACTIVE SYSTEMS: DESIGN, SPECIFICATION, AND VERIFICATION

Vol. 2

Pages: 447-455

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-000-5X2

Abstract (EN): Image classification is often used to extract information from multi-spectral satellite images. Unsupervised methods can produce results well adjusted to the data, but that are usually difficult to assess. The purpose of this work was to evaluate the Xu internal similarity index ability to estimate the natural number of classes in multi-spectral satellite images. The performance of the index was initially tested with data produced synthetically. Four Landsat TM image sections were then used to evaluate the index. The test images were classified into a large number of classes, using the unsupervised algorithm ISODATA, which were subsequently structured hierarchically. The Xu index was used to identify the optimum partition for each test image. The results were analysed in the context of the land cover types expected for each location.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9