Winter wheat planting area identification and extraction based on image segmentation and NDVI time series curve classification model

doi:10.6046/gtzyyg.2020.02.28

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Abstract

For the purpose of automatically obtaining a large area of winter wheat planting area, phenological information in medium spatial resolution remote sensing images based on time series curves is usually used to identify and extract. However, in actual engineering projects, if phenological information is used only, the accuracy is low. Therefore, a method based on time series curve data classification model and image segmentation is proposed for winter wheat identification. Firstly, the normalized difference vegetation index (NDVI) time series curve of multi-source data is constructed, and the NDVI time series data are smoothed and denoised by harmonic analysis of time series (HANTS) method. Then, via coordinate transformation of NDVI time series, three parameters of band mean, standard deviation and square mean are obtained to construct a new classification model so as to improve the difference between winter wheat and other crops; finally, by combining segmentation results of spatial resolution data, spatial structure information of the image is used to improve accuracy of feature boundary. Taking Jiangning District of Nanjing as an example, the authors used 21 multi-source images of GF-1, Landsat8 and Sentinel-2A from December 2017 to June 2018, and the final extraction accuracy reached 98.74%, which is better than results of other methods. This method provides agricultural management departments with accurate geographic information data on planting area and distribution of winter wheat.

Keywords NDVI time series curve image segmentation remote sensing classification model winter wheat extraction

TP79

Corresponding Authors: Wenquan HAN E-mail: lidar_hwq@163.com

Issue Date: 18 June 2020

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	Biqing WANG
	Wenquan HAN
	Chi XU

Cite this article:

Biqing WANG,Wenquan HAN,Chi XU. Winter wheat planting area identification and extraction based on image segmentation and NDVI time series curve classification model[J]. Remote Sensing for Land & Resources, 2020, 32(2): 219-225.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.28 OR https://www.gtzyyg.com/EN/Y2020/V32/I2/219

Tab.1 Data in experiment

Fig.1 BJ-2 satellite image of study area

Fig.2 Technical route

Fig.3 NDVI time series curve Filter comparison chart

Fig.4 Coordinate conversion NDVI value curves

Fig.5 NDVI time series curve classification model result

Fig.6 Image segmentation results

Fig.7 Contrast of before and after integration

Fig.8 Spatial distribution of wheat in Jiangning District, Nanjing

Fig.9 Verification sample point distribution of Jiangning District, Nanjing

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