Research On Fine Classification Technology Of Tree Species Based On GF-2 Remote Sensing Data

In order to explore the application potential of domestic high-resolution data in the classification of tree species,this paper studies the determining method of multiresolution segmentation parameters and the method of feature optimization based on GF-2 and ground survey data in part of the state-owned forest areas in the Northeast and Inner Mongolia.In addition,the error analysis of the sub-compartment dominant tree species extracted from the classification results is also analyzed which providing a reliable reference for domestic high-resolution data forestry applications.The main research work of this article are as follows.(1)The GF-2 multispectral and full-color image are used as data sources,and two common segmentation algorithms,watershed transform segmentation and multiresolution segmentation,are used for image segmentation experiments.The results show that the image objects segmented by the multiresolution segmentation algorithm retain better forest features,and have obvious advantages compared with the watershed transform segmentation algorithm.In the multiresolution segmentation algorithm parameters determination stage,this paper controls single factor to determine the optimal value of the shape factor and the compactness factor of the homogeneity criterion parameters.Then,using the optimal segmentation scale estimation tool ESP2,the optimal segmentation scales are calculated with the combination of two factors.And then point index of optimal segmentation scale evaluation method based on tree species sample point pairs is used to evaluate the results of different scale segmentation effects.After determining the optimal segmentation scale,the experimental inverse verification of the optimal homogeneity criterion parameters segmentation effect under the scale parameter is carried out.The combination of forward experiment and reverse verification is used to determine the best parameters for image segmentation.(2)The high spatial resolution of the sensor increases the feature dimensions of the image segmentation object.To solve this problem,the statistical analysis method and the modeling selection method are used to find the feature set required by several classification methods.The results show that the method of modeling selection determining the feature contribution has higher classification accuracy in the later classification.On this basis,the paper attempts to select the feature set by the two-class modeling,which improves the overall accuracy of the classification results of the random forest method by 3.57%and the Kappa coefficient by 0.0406.(3)After analyzing the difference between the dominant tree species of sub-compartment recorded by the forest resource planning and design survey and extracted by classification results,this study finds that the tree species fine classification technique extracts the dominant tree species in the sub-compartment with a large proportion of the single tree species more prominently.In the area where the stand is mixed seriously,the ability to extract the dominant species from the classification results is poor.