期刊论文

Zhou, C

英文

Forests

1999-4907

2024

15

3

456-

10.3390/f15030456

Conceptualization, J.Z. and C.Z.; methodology, J.Z., C.Z. and G.Z.; validation, J.Z., C.Z., G.Z., Z.Y., Z.P. and Y.L.; formal analysis, J.Z., C.Z. and G.Z.; investigation, J.Z., C.Z., G.Z., Z.Y. and Y.L.; resources, Z.P.; data curation, G.Z.; writing—original draft preparation, J.Z.; writing—review and editing, J.Z. and C.Z.; supervision, C.Z.; project administration, C.Z., G.Z., Z.Y. and Y.L.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 42074016 and 42030112).

本校为通讯机构

The estimation of forest above-ground biomass (AGB) can be significantly improved by leveraging remote sensing (RS) and deep learning (DL) techniques. In this process, it is crucial to obtain appropriate RS features and develop a suitable model. However, traditional methods such as random forest (RF) feature selection often fail to adequately consider the complex relationships within high-dimensional RS feature spaces. Moreover, challenges related to parameter selection and overfitting inherent in DL models may compromise the accuracy of AGB estimation. Therefore, this study proposes a novel f...