Prof. Yufeng Liu Profile

Prof. Yufeng Liu

at Chuzhou Univ

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SPIE Journal Paper | 1 October 2024

Crop type identification using spatio-temporal fusion of multi-source remote sensing data based on time-weighted dynamic time warping

Sifan Fang, Hu Li, Yufeng Liu, Xinhua Liu, Yingmei Hu, Ao Xu

JARS, Vol. 18, Issue 04, 044501, (October 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.044501

KEYWORDS: Remote sensing, Phenology, Data fusion, Image classification, Image fusion, Education and training, Clouds, Vegetation, Synthetic aperture radar, Polarization

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The spatiotemporal distribution information of crops is essential. However, remote sensing mapping still faces challenges, such as fragmented land plots, cloud cover, and changes in crop morphology across the phenological stages. To address these issues, we constructed a time series multi-feature dataset using the flexible spatio-temporal data fusion method, which fused Sentinel-2 and MODIS data, with the addition of Sentinel-1 SAR data. We employed the time-weighted dynamic time warping (TWDTW) algorithm and compared its performance with the random forest (RF) algorithm. By comparing the crop classification results of different lengths of time-series multi-feature datasets, we investigate the earliest identifiable time for crops. The results demonstrate that the incorporation of multi-source time-series remote sensing data in the construction of crop phenological features effectively improves crop classification accuracy. The TWDTW algorithm attained an overall accuracy of 92.28% and a Kappa coefficient of 0.895, outperforming the RF method, which had an accuracy of 90.44% and a Kappa coefficient of 0.869. Early-stage time-series data was enough to identify wheat, whereas complete time-series data for the entire growing season was required for oilseed rape and fallow fields. In the early stages of identification, using spatial-temporal fusion can improve crop recognition accuracy. This study found that the spatio-temporal fusion of multi-source remote sensing data and TWDTW offers the potential for accurate crop classification, especially in areas with complex fragmented cropping systems.

Proceedings Article | 4 April 2023 Paper

Spectral characteristics analysis of typical crops in different growth periods based on spectral domain interpolation

Li Xusheng, Yakui Shao, Chenchen Jiang, Yingjun Zhao, Kai Qin, Donghui Zhang, Yufeng Liu

Proceedings Volume 12617, 126176U (2023) https://doi.org/10.1117/12.2666748

KEYWORDS: Interpolation, Remote sensing, Reflectivity, Analytical research, Phenology, Agriculture, Feature extraction

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Under the influence of the physicochemical characteristics of the crops and the space-time environmental factors, even the same crop will show some oscillation in the spectrum. Previous studies mostly used arithmetic mean value to reduce the uncertainty caused by spectral oscillation, but the characterization ability of mean value is susceptible to the degree of numerical difference. To solve these problems and to explore the relationship between the accuracy of typical crop identification and the growth period and spectral characteristics, we proposed a spectral construction algorithm based on Spectral Domain Interpolation (SDI). Using SDI and traditional Arithmetic Mean (AM) method, the characteristic spectra of typical crops (winter wheat, oilseed grape) and the main background vegetation (trees, grasses, rice stubble) were constructed at different growth stages (March, April and May). Then, feature parameters were extracted based on the constructed characteristic spectrum. The importance evaluation and linear discriminant analysis of the extracted feature parameters were carried out at last. The optimal identifying growth period and identifying feature parameters of typical crops were obtained through comparative analysis, at the same time, the availability and superiority of SDI were verified. The following conclusions were drawn: (1) SDI has a good resistance to extremes, and can retain the spectral characteristics of crops well, and construct a more characterizing characteristic spectrum. (2) The best identifying growth periods of oilseed grape and winter wheat are early flowering period in March and heading period in April, respectively. The best identifying characteristics of winter wheat and oilseed grape are yellow edge position and red edge amplitude, respectively. (3) Winter wheat and oilseed grape can be well identified by using the position of the yellow edge in March and the blue edge area and the red edge amplitude in April.

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