Object-based forest type classification with ZY-3 remote sensing data.

Abstract

A new method of identification technology for forest types, an important and difficult part of remote sensing classification, uses object-oriented remote sensing image classification.It provides a new direction for forest type to extract which is based on ZY-3 remote sensing data. This study applied ZY-3 remote sensing data to the object-oriented classification method, chose hierarchical segmentation of a fractal network as an evolution method, and combined typical ground objects including spectrum features, texture features, geometrical characteristics, and vegetation indexes, to build a decision tree model which is applicable to forest types. Then, the different segmentation scale compared from the support vector machine (SVM) classification method. Results showed that classification accuracy of the decision tree classification method with multi-level segmentation (which increased 6.1% and 12.5% ) was higher than the support vector machine (SVM) classification method with the different single segmentations. Thus, it would be suitable to build a decision tree classification with multi-level segmentation to the classification of forest type.