面向对象多尺度分割的SPOT5影像毛竹林专题信息提取

doi:10.11707/j.1001-7488.20131013

Abstract

Abstract:

Based on SPOT5 remotely sensed imagery, this research focused on delineating moso bamboo forest using object-based method, which provided the advantages of multi-scale segmentation and developing hierarchical structure. The results showed that: 1) The most appropriate window sizes for calculating texture using red (R), green (G) and blue (B) band in SPOT5 image were 9×9,7×7,9×9; 2) Extracting moso bamboo using multi-scale segmentation technique of object-based method was more accurate, with the producer's accuracy reaching 90%, obviously higher than that of the conventional maximum likelihood method(88.57%); 3) Multiresolution segmentation with the aid of texture not only ensured the accuracy of moso bamboo, but also provided help to the other forest types. The overall accuracy was 92% and the Kappa coefficient was 88.14%, both of which were the highest accuracy in the present study.

Key words: object-oriented, multi-scale segmentation, moso bamboo forest, information extraction, SPOT5

CLC Number:

S771.8

Sun Xiaoyan, Du Huaqiang, Han Ning, Ge Hongli, Gu Chengyan. Multi-Scale Segmentation, Object-Based Extraction of Moso Bamboo Forest from SPOT 5 Imagery[J]. Scientia Silvae Sinicae, 2013, 49(10): 80-87.

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Multi-Scale Segmentation, Object-Based Extraction of Moso Bamboo Forest from SPOT 5 Imagery

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