A Procedure for Semi-automatic Segmentation in OBIA Based on the Maximization of a Comparison Index

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• 作者：Andres Auquilla (23) (25)
  Stien Heremans (24)
  Pablo Vanegas (23)
  Jos Van Orshoven (24)
  
• 关键词：OBIA ; segmentation ; classification ; support vector machines ; segmentation parameters ; comparison index
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8579
• 期：1
• 页码：360-375
• 全文大小：3,489 KB
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• 作者单位：Andres Auquilla (23) (25)
  Stien Heremans (24)
  Pablo Vanegas (23)
  Jos Van Orshoven (24)
  
  23. Computer Science Department, Universidad de Cuenca, Cuenca, Ecuador
  25. Centre for Industrial Management, Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300A, B-3000, Leuven, Belgium
  24. Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, B-3001, Leuven, Belgium
  
• ISSN：1611-3349

文摘

In an Object Based Image Analysis Classification (OBIA) process, the quality of the classification results are highly dependent on segmentation. However, a high number of the studies that make use of an OBIA process find the segmentation parameters by making use of trial-and-error methods. It is clear that a lack of a structured procedure to determine the segmentation parameters produces unquantified errors in the classification. This paper aims to quantify the effects of using a semi-automatic approach to determine optimal segmentation parameters. To this end, an OBIA process is performed to classify land cover types produced by both a manual and an automatic segmentation. Even though the classification using the manual segmentation outperforms the automatic segmentation, the difference is only 2%. Since the automatic segmentation is performed with optimal parameters, a procedure to accurately determine those parameters must be performed to minimize the error produced by a misjudgment in the segmentation step.