Research on the inversion method of vegetation cover in Liupan Mountain area based on spatiotemporal fusion algorithm

Mengyu Chen; Xuehong Sun; Linhao Wang; Liping Liu; Xianbin Wang

doi:10.1117/12.3057523

28 January 2025 Research on the inversion method of vegetation cover in Liupan Mountain area based on spatiotemporal fusion algorithm

Mengyu Chen, Xuehong Sun, Linhao Wang, Liping Liu, Xianbin Wang

Author Affiliations +

Proceedings Volume 13506, Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024); 135060M (2025) https://doi.org/10.1117/12.3057523
Event: Sixth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2024), 2024, Qingdao, China

Abstract

This paper proposes a spatio-temporal fusion model based on ESRCNN-ScConv, which combines spatial and channel reconstruction techniques with the objective of optimising feature extraction. The enhanced hybrid image decomposition model comprises two key components: multi-exponential end-element extraction and the least squares residual method. The generation of high-resolution data images is achieved through the application of the classical and extended ESRCNN-ScConv spatiotemporal fusion algorithm models. The inversion of the vegetation cover of the area is conducted using the classical model and the improved hybrid image element decomposition model. The experiment demonstrates that the model exhibits superior performance compared to the traditional model in terms of specificity, R², and other indexes. This validates the model's efficacy and practicality, and provides a novel concept and effective technical foundation for advancing the methodological research of vegetation cover.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mengyu Chen, Xuehong Sun, Linhao Wang, Liping Liu, and Xianbin Wang "Research on the inversion method of vegetation cover in Liupan Mountain area based on spatiotemporal fusion algorithm", Proc. SPIE 13506, Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024), 135060M (28 January 2025); https://doi.org/10.1117/12.3057523

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