Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May 15;20(5):e0323446.
doi: 10.1371/journal.pone.0323446. eCollection 2025.

A fast hyperspectral change detection algorithm for agricultural crops based on spatial reconstruction

Jianghong Yuan  1   2 , Er-Yang Chen  3   4 , Haiyin Qing  2
Affiliations

A fast hyperspectral change detection algorithm for agricultural crops based on spatial reconstruction

Jianghong Yuan et al. PLoS One. .

Abstract

Crop change detection plays a pivotal role in ensuring agricultural sustainability and environmental monitoring. Leveraging the high spectral resolution of hyperspectral imagery and bi-temporal analysis, this study presents a Fast Hyperspectral Change Detection algorithm based on Spatial Reconstruction (FHCDSR) designed to identify subtle agricultural changes with improved accuracy and computational efficiency. The proposed method incorporates three key innovations: (1) boundary-constrained preprocessing of 3D hyperspectral data, (2) Laplacian-regularized spatial reconstruction, and (3) a novel tensor-based change detection framework. We conduct a comprehensive evaluation of FHCDSR using two datasets: the Hermiston dataset and the Yancheng dataset. Experimental results demonstrate that FHCDSR achieves superior performance on both datasets, with AUC values of 90.20% (Hermiston) and 95.39% (Yancheng), outperforming six state-of-the-art comparison methods by 3.39-14.78% in detection accuracy. Remarkably, the algorithm maintains high computational efficiency, completing analyses in 9.76 seconds (Hermiston) and 10.90 seconds (Yancheng), representing up to 94.05% reduction in processing time compared to conventional methods. The consistent performance across different agricultural landscapes highlights FHCDSR's robustness as an unsupervised change detection solution, with significant potential for precision agriculture and wetland ecosystem monitoring applications.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Illustration of the Hermiston dataset.
(A) Farmland on 1 May 2004, (B) Farmland on 8 May 2007, (C) Ground-truth change map.
Fig 2
Fig 2. Flowchart of the method.
Fig 3
Fig 3. ROC curves for different methods int the Hermiston dataset.
Fig 4
Fig 4. ROC curves for different methods int the Yancheng dataset.
See this image and copyright information in PMC

References

    1. Wang D, Saleh NB, Byro A, Zepp R, Sahle-Demessie E, Luxton TP, et al.. Nano-enabled pesticides for sustainable agriculture and global food security. Nat Nanotechnol. 2022;17(4):347–60. doi: 10.1038/s41565-022-01082-8 - DOI - PMC - PubMed
    1. Hasanlou M, Seydi ST. Hyperspectral change detection: an experimental comparative study. Int J Remote Sens. 2018;39(20):7029–83.
    1. Liu S, et al.. A review of change detection in multitemporal hyperspectral images: current techniques, applications, and challenges. IEEE Geosci Remote Sens Mag. 2019;7(2):140–58.
    1. Hou Z, Zhang Y, Li J, Wang X, Zhao Y. Hyperspectral change detection based on multiple morphological profiles. IEEE Trans Geosci Remote. 2021;60:1–12.
    1. Eismann MT, Meola J, Hardie RC. Hyperspectral change detection in the presenceof diurnal and seasonal variations. IEEE Trans Geosci Remote Sens. 2008;46(1):237–49. doi: 10.1109/tgrs.2007.907973 - DOI

MeSH terms

LinkOut - more resources