Study on Forest Vegetation Classification Based on Multitemporal Remote Sensing Images
نویسندگان
چکیده
It is very difficult to classify forest vegetation in mountain areas because of the impact of complex terrain. A new method, classification of forest vegetation based on multi-temporal remote sensing, is proposed in this paper. The forest vegetation could get better classification precision by avoiding the interactions of different plants with multi-temporal images. So it enhanced the separability of coniferous forest and broadleaf forest. The classification result showed that the accuracy could be greatly improved by using multi-temporal remote sensing images. The overall accuracy and kappa coefficient were 81.3% and 0.72, respectively. So the method delivered in this essay has obviously technological advantages and important application potentiality in forest vegetation classification.
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