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Journal of Applied Remote Sensing • Open Access

Synergistic use of Landsat TM and SPOT5 imagery for object-based forest classification
Author(s): Xiaoyan Sun; Huaqiang Du; Ning Han; Guomo Zhou; Dengsheng Lu; Hongli Ge; Xiaojun Xu; Lijuan Liu

Paper Abstract

This study evaluated the synergistic use of Landsat5 TM and SPOT5 images for improving forest classification using an object-based image analysis approach. Three image segmentation schemes were examined: (1) segmentation based on both SPOT5 and Landsat5 TM; (2) segmentation based solely on SPOT5; and (3) segmentation based solely on Landsat5 TM. The optimal scale parameters based on TM/SPOT5 and SPOT5 were determined by measuring the topological similarity between segmented objects and reference objects at 10 different scales. Mean and standard deviation of the pixels within each object in each input layer were the classification metrics. Nearest neighbor classifier was performed for the three segmentation schemes. The results showed that (1) the optimal scales of TM/SPOT5, SPOT5, and TM were 70, 100, and 0.8, respectively and (2) classification results with medium spatial resolution images were not desirable, with overall accuracy of only 72.35%, while synergistic use of Landsat5 TM and SPOT5 greatly improved forest classification accuracy, with overall accuracy of 82.94%.

Paper Details

Date Published: 29 September 2014
PDF: 15 pages
J. Appl. Rem. Sens. 8(1) 083550 doi: 10.1117/1.JRS.8.083550
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Xiaoyan Sun, Zhejiang Forestry Univ. (China)
Huaqiang Du, Zhejiang Forestry Univ. (China)
Ning Han, Zhejiang Forestry Univ. (China)
Guomo Zhou, Zhejiang Forestry Univ. (China)
Dengsheng Lu, Zhejiang Forestry Univ. (China)
Hongli Ge, Zhejiang Forestry Univ. (China)
Xiaojun Xu, Zhejiang Forestry Univ. (China)
Lijuan Liu, Zhejiang Forestry Univ. (China)

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