哨兵一号协同吉林一号影像的树种识别研究.

Aiming at the serious problem of de - coherence in the forest classification of the C - band synthetic aperture radar im¬age in conjunction with the optical image, a method of averaging coherence coefficient images in multiple periods in winter obtained by coherent imaging of similar time - phase radar images is proposed to effectively suppress the de - coherence phenomenon to obtain co¬herence coefficient images related to forest tree species. This paper uses the Sentinel - 1 backscattering intensity, coherence coefficient and JL101A image to classify the tree species of the moon lake national forest park in Changchun. The results show that when using on¬ly JL101A for classification, the overall classification accuracy is 82. 3% , Kappa coefficient is 0. 79； when using JL101A data and Sentinel - 1 intensity data for classification, the overall classification accuracy is 85.2% , and the Kappa coefficient is 0. 825 ； when u- sing JL101A data, Sentinel -1 intensity data and coherence data for classification, the overall classification accuracy is 87. 8% , and the Kappa coefficient is 0. 855. After using JL101A data, Sentinel - 1 intensity data and coherence data to classify, compared with u- sing only JL101A data, the precision of user accuracy is increased from 59% to 72%. It shows that the optical image combined with the effective coherence coefficient and backscattering intensity of C band synthetic aperture radar image can improve the classification accuracy of forest tree species. [ABSTRACT FROM AUTHOR]