Obtaining farmland information from high resolution remote sensing satellite images

XingYu Mou; ShuKun Jin; Xin Wang; Hui Chen; Chengcheng Wang; Xin Xu; Ying Zhang; Linan Chen; RuiXiang Song

doi:10.1117/12.3033350

29 July 2024 Obtaining farmland information from high resolution remote sensing satellite images

XingYu Mou, ShuKun Jin, Xin Wang, Hui Chen, Chengcheng Wang, Xin Xu, Ying Zhang, Linan Chen, RuiXiang Song

Proceedings Volume 13214, Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024); 1321405 (2024) https://doi.org/10.1117/12.3033350
Event: Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024), 2024, Guangzhou, China

Abstract

This article proposes a method for obtaining farmland information in high-resolution satellite images. Firstly, divide the pixels of satellite images into a set of soft object regions and correspond them to a class. Secondly, the representation of each object region is estimated by aggregating the classes of pixels in the object region. Then, design the Hamacher operator decision method to make decisions on the neutral fuzzy part. Finally, calculate the relationship weights and weighted aggregation between the target regions, with the highest aggregation being the category of the pixel region. The experimental results show that the proposed method can effectively obtain cultivated land information from high-resolution satellite images. Meanwhile, compared with existing mature methods, the scoring values of our method have significantly improved.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

XingYu Mou, ShuKun Jin, Xin Wang, Hui Chen, Chengcheng Wang, Xin Xu, Ying Zhang, Linan Chen, and RuiXiang Song "Obtaining farmland information from high resolution remote sensing satellite images", Proc. SPIE 13214, Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024), 1321405 (29 July 2024); https://doi.org/10.1117/12.3033350

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