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Expert Systems with Applications
Volume 32, Issue 2, February 2007, Pages 616-624
 
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doi:10.1016/j.eswa.2006.01.055    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Elsevier Ltd All rights reserved.

An automated satellite image classification design using object-oriented segmentation algorithms: A move towards standardization

Ruvimbo GamanyaCorresponding Author Contact Information, a, E-mail The Corresponding Author, Philippe De Maeyera and Morgan De Dappera

aDepartment of Geography, Gent University, Krijgslaan 281, S8, B-9000 Gent, Belgium

Available online 28 February 2006.

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Abstract

Numerous segmentation algorithms have been developed, many of them highly specific and only applicable to a reduced class of problems and image data. Without an additional source of knowledge, automatic image segmentation based on low level image features seemed unlikely to succeed in extracting semantic objects in generic images. A new region-merging segmentation technique has recently been developed which incorporates the spectral and textural properties of the objects to be detected and also their different size and behaviour at different stages of scale, respectively. Linking this technique with the FAO Land Cover Land Use classification system resulted in the development of an automated, standardized classification methodology. Testing on Landsat and Aster images resulted in mutually exclusive classes with clear and unambiguous class definitions. The error matrix based on field samples showed overall accuracy values of 92% for Aster image and 89% for Landsat. The KIA values were 88% for Aster images and 84% for the Landsat image.

Keywords: Object-orientation; Land use and land cover; Automation; Standardization

Article Outline

1. Introduction
2. Background
2.1. A ‘new’ paradigm—multi-scale image segmentation
2.1.1. Form heterogeneity
2.2. LCCS technical concepts
2.3. Similarities between LCCS and eCognition
3. Methods
3.1. Data acquisition and preprocessing
3.2. Feature selection
3.3. Study area
3.4. Field reconnaissance
3.5. Automation of methodology
4. Results and discussion
4.1. Protocol development
4.2. LCCS classifiers
4.3. Validation using Landsat image
4.4. Standardization of methodology
4.5. Automation of method
5. Summary and future directions
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