Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images

Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images

Image segmentation is an important process and a prerequisite for object-based image analysis. Thus, evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and to optimize the scale. In this paper, we propose an unsupervised evaluation (UE) meth...

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Bibliographic Details
Main Authors: Yongji Wang, Qingwen Qi, Ying Liu
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Remote Sensing
Subjects:
image segmentation
unsupervised evaluation
remote sensing
area-weighted variance
Jeffries-Matusita distance
geographic object-based image analysis
Online Access:http://www.mdpi.com/2072-4292/10/8/1193

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http://www.mdpi.com/2072-4292/10/8/1193

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