Cluster Validity Classification Approaches Based on Geometric Probability and Application in the Classification of Remotely Sensed Images
| Autor: | LI Jian-Wei, LI Xiao-Wen, MAO Zheng-Yuan, KONG Xiang-Zeng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Sensors & Transducers, Vol 177, Iss 8, Pp 128-135 (2014) |
| ISSN: | 1726-5479 2306-8515 |
| Popis: | On the basis of the cluster validity function based on geometric probability in literature [1, 2], propose a cluster analysis method based on geometric probability to process large amount of data in rectangular area. The basic idea is top-down stepwise refinement, firstly categories then subcategories. On all clustering levels, use the cluster validity function based on geometric probability firstly, determine clusters and the gathering direction, then determine the center of clustering and the border of clusters. Through TM remote sensing image classification examples, compare with the supervision and unsupervised classification in ERDAS and the cluster analysis method based on geometric probability in two-dimensional square which is proposed in literature 2. Results show that the proposed method can significantly improve the classification accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|