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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (4) : 105-110     DOI: 10.6046/gtzyyg.2020.04.15
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A study of crop planting type recognition based on SAR and optical remote sensing data
JIANG Shan1,2(), WANG Chun2,3, SONG Hongli1, LIU Yufeng2
1. School of Geosciences and Engineering, Hebei University of Engineering, Handan 056000, China
2. Anhui Key Laboratory of Physical Geography and Environment of Chuzhou University, Chuzhou 239000, China
3. School of Remote Sensing and Mapping Engineering, Nanjing University of Information Engineering, Nanjing 210044, China
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Abstract  

In order to acquire the appropriate remote sensing data to obtain the plant growth information and identify the planting types of crops, the authors chose Quanjiao of Chuzhou in Anhui Province as the research area and the SAR (GF-3) data and optical remote sensing data as the data source to fuse optical data with the SAR data and make a comparative study of data classification results, optical and SAR data classification results and the data fusion results so as to conduct crop type identification. The comparison of the data of classification results reveals that SAR data can be used as a good auxiliary optical image for crop planting types in crop recognition. The fusion of SAR data and optical remote sensing data has a good identification effect on crops in the research area.
Keywords synthetic aperture Radar (SAR)      crop recognition      fusion      classification     
:  TP79  
Issue Date: 23 December 2020
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Shan JIANG
Chun WANG
Hongli SONG
Yufeng LIU
Cite this article:   
Shan JIANG,Chun WANG,Hongli SONG, et al. A study of crop planting type recognition based on SAR and optical remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 105-110.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.04.15     OR     https://www.gtzyyg.com/EN/Y2020/V32/I4/105
Fig.1  Schematic diagram of study area
Fig.2  Research area after SAR data processing
农作物波段 1号 2号 3号 4号 5号 6号 7号 8号
油菜蓝光波段 626 631 672 672 710 728 730 769
小麦蓝光波段 493 540 552 557 560 562 563 592
油菜绿光波段 928 979 992 992 1 010 1 027 1 059 1 167
小麦绿光波段 685 728 734 762 777 835 836 869
油菜红光波段 1 293 1 327 1 347 1 379 1 391 1 419 1 485 1 496
小麦红光波段 1 020 1 027 1 041 1 068 1 193 1 196 1 198 1 250
油菜近红外波段 2 513 2 526 2 605 2 660 2 666 2 681 2 690 2 749
小麦近红外波段 1 731 1 909 2 070 2 373 2 439 2 587 2 654 2 630
Tab.1  Reflectance value of wheat and rape of different waves
Fig.3  Spectral reflectance curve of crops
Fig.4  Regional achievement by optical remote sensing classification
土地覆盖
类型		 1月中旬 2月中旬 3月中旬 4月中旬 5月中旬
VV VH VV VH VV VH VV VH VV VH
小麦 -8.2 -14.8 -9.6 -15.5 -11.1 -16.2 -11.9 -16.0 -10.0 -13.9
油菜 -5.4 -12.4 -6.4 -12.7 -7.4 -12.8 -6.3 -11.4 -5.3 -10.9
Tab.2  Comparison of backscatter of wheat and rape with different polarization
Fig.5  Line chart of different polarization backscattering of wheat and rape
Fig.6  Local result by SAR data classification
Fig.7  Comparison before and after data fusion
Fig.8  Local result after image classification fusion based on HSV method
Fig.9  Local result after image classification based on Gram-Schmid fusion
土地覆盖类型 训练样本像素数 验证样本
水体 5 181 4 565
裸地 1 605 1 422
林地 3 781 3 238
居民地 2 071 2 272
Tab.3  Pixel number of training and verification samples for reference figures(个)
土地覆盖类型 训练样本像素数 验证样本
小麦 2 003 277
油菜 976 778
Tab.4  Pixel number of training samples and validation samples of wheat and rape(个)
类别 光学 SAR HSV融合 Gram-Schmidt
融合
小麦用户精度/% 41.24 27.84 80.36 63.66
油菜用户精度/% 37.10 14.67 96.79 69.32
小麦漏分误差/% 43.04 51.17 12.52 24.51
油菜漏分误差/% 64.65 73.26 30.61 41.79
小麦制图精度/% 56.96 48.83 87.48 75.49
油菜制图精度/% 35.35 26.74 69.39 58.21
总体分类精度/% 73.02 58.57 86.29 96.20
Kappa系数 0.659 7 0.477 2 0.816 4 0.949 0
Tab.5  Classification accuracy evaluation
Fig.10  Classification results
Fig.11  Local results of wheat and rape classification
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