文献类型：期刊文献

英文题名：The Forest above Ground Biomass Estimation Based on Multi-Feature Combination Method Using Multi-Frequency SAR Data

作者：Ma, Yunmei[1] Zhao, Lei[1] Chen, Erxue[1] Li, Zengyuan[1] Fan, Yaxiong[1] Xu, Kunpeng[1]

第一作者：Ma, Yunmei

机构：[1] Chinese Academy of Forestry, Institute of Forest Resources Information Technique, China

年份：2024

起止页码：4978-4981

外文期刊名：International Geoscience and Remote Sensing Symposium (IGARSS)

收录：EI(收录号：20243917115982)

语种：英文

摘要：In this paper, we studied the multi-feature combination estimation approach of forest above ground biomass (AGB) using X-band InSAR and P-band PolInSAR data. We focus on a crucial step of the estimation process, which is selection of the optimal feature combination. Firstly, the feature pool was acquired using multi-frequency SAR data, which includes optimized features (forest height and polarimetric interferometric feature) and original features (polarimetric features, intensity features, and texture features). Then, using machine learning method to select the optimal feature combination. Finally, the forest AGB was estimated based on multiple types of the features combination. The experimental results showed that the combination of optimized features with original features has the highest accuracy in forest AGB estimation, followed by the combination using only optimized features. The accuracy of forest AGB estimation is lower for the feature combination that does not include optimized features. ? 2024 IEEE.