Using Landsat7 ETM+ images as ancillary data for forest cover classification of SPOT5 images
1 Guangxi Forest Inventory and Planning Institute, Nanning, 530011, P. R. China; 2 Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN47906, USA.
More Information
Received Date:
December 31, 1899
Revised Date:
December 31, 1899
Published Date:
July 29, 2010
Abstract
In order to improve the accuracy of object-oriented classification of SPOT5 images, Landsat7 ETM+ images were used as ancillary data in three protocols of image segmentation and object feature extraction. Using Bayes rule, the classification of images was performed, followed by image segmentation, rule-based classification, classification-based segmentation, object-feature extraction and selection. The results indicate that the application of ETM+ data can improve the classification accuracy of SPOT5 images. However, with low spatial resolution, this protocol can only be used in the extraction of object features, and not in image segmentation in regions with fragmented forests, diverse species, types, and complex structures. Such conditions lower the precision of object borders, reduce homogeneity and increase the heterogeneity of objects, which in turn affect the classification accuracy.
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