Random forest classification of Callicarpa nudiflora from WorldView-3 imagery based on optimized feature space.
10.19540/j.cnki.cjcmm.20190731.104
- Author:
Ting-Ting SHI
1
;
Xiao-Bo ZHANG
1
;
Lan-Ping GUO
1
;
Lu-Qi HUANG
1
Author Information
1. State Key Laboratory Breeding Base of Dao-di Herbs,National Resource Center for Chinese Materia Medica,China Academy of Chinese Medical Sciences Beijing 100700,China.
- Publication Type:Journal Article
- Keywords:
Callicarpa nudiflora;
WorldView-3;
feature space optimization;
information extraction;
random forest
- MeSH:
Algorithms;
Callicarpa;
Plants, Medicinal
- From:
China Journal of Chinese Materia Medica
2019;44(19):4073-4077
- CountryChina
- Language:Chinese
-
Abstract:
Taking the Xiushui township of Baisha county in Hainan province as the research area,the random forest algorithm with obvious advantages in feature selection and classification extraction was used to extract the information of the Callicarpa nudiflora planting in the study area. Firstly,four kinds of different characteristic variables were generated based on World View-3 data,including spectral features,principal component features,vegetation index and texture features. Secondly,the spatial distribution of the C. nudiflora in the study area was extracted by remote sensing by random forest classification algorithm. Finally,the feature space of the random forest classification algorithm was optimized based on the feature importance to obtain the best random forest classification results,and this result is compared with the classification result of the random forest algorithm of the unoptimized feature space. The results showed that:①The overall accuracy of the C. nudiflora extracted by World View-3 image was 89. 97%,and the Kappa coefficient was 0. 84,which indicates that the random forest algorithm had higher classification accuracy and better applicability in Hainan C. nudiflora recognition.② The overall accuracy of extracting C. nudiflora with the dimension reduction feature was 90. 4,and the Kappa coefficient was 0. 85,which indicates that the random forest algorithm can effectively select features. At the same time as the feature variable data mining,the precision of the information extraction of the C. nudiflora was still guaranteed,and the operation efficiency was improved. This study provides a new idea,method and technical means for information extraction of cultivated medicinal plant resources in terms of feature selection and method selection.
