基于多时相多极化差值图的稻田识别研究
Rice Mapping Research Based on Multi-temporal, Multi-polarization Backscattering Differences
- 2008年第4期 页码:613-619
纸质出版日期: 2008
DOI: 10.11834/jrs.20080480
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纸质出版日期: 2008 ,
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[1]杨沈斌,李秉柏,申双和,谭炳香,何维.基于多时相多极化差值图的稻田识别研究[J].遥感学报,2008(04):613-619.
YANG Shen-bin1, LI Bing-bai2, SHEN Shuang-he1, et al. Rice Mapping Research Based on Multi-temporal, Multi-polarization Backscattering Differences[J]. Journal of Remote Sensing, 2008,(4):613-619.
提出了一种基于多时相多极化差值图的稻田识别方法
该方法在简化稻田识别算法的同时
仍具有较好的稻田识别精度。以江西省高安地区的早稻识别为例
利用两景ENV ISAT ASAR交叉极化模式数据(VV/HH)计算了同时相多极化差值图和同极化多时相差值图。由于稻田含有水层和水稻的垂直株型等属性特征
稻田在两时相上VV极化和HH极化后向散射差异都很大
且与其他地物具有明显差别
因此利用同时相多极化差值图可以很好地分辨出稻田来;从时间变化看
HH极化雷达波对水稻生长和稻田的变化比对其他地物的变化更敏感
使稻田分布信息在HH极化多时相差值图中反应突出。而VV极化对地物的时相变化不够敏感。因此
建立最优差值图组合
分别采用阈值分类方法和监督分类方法对差值图组合进行分类提取稻田。通过比较分类结果
认为基于统计分析的监督分类方法更好
其稻田识别的精度达到84.92%。文章最后对提出的稻田识别方法及分类结果进行了分析。
Since the launch of ENVISAT satellite in 2002
a considerable amount of programs has been set up in application of the new generation of Advanced Synthetic Aperture Radar(ASAR) instrument for monitoring agricultural crops
which extends the mission of SAR instruments onto ERS-1/2 and provides the continuity on agricultural monitoring.Under the framework of Dragon Project
which is a three-year(2004—2007) cooperation program between NRSCC and ESA focused on science and applications development in China mainly using data from the ERS and ENVISAT missions
a project theme on rice monitoring was set and endeavored to validate the efficiency of ASAR data for rice monitoring in China.Under these circumstances
this paper intends to propose a practical method for rice mapping
taking advantage of ASAR Alternative Polarization Mode(APMode) product which allows the acquisition of radar images with two simultaneous polarizations selected from the four polarizations HH
HV
VH and VV.Therefore
during the early rice season of Gaoan district of Jiangxi Province in 2006
two scenes of ASAR APMode products were acquired on the date of May 8 and June 12
in a specific radar configuration of HH and VV polarization and at approximately 40° incidence angle.Meanwhile
six differential GPS samples containing different crops and other land surface objects
with size of 1 km×1 km for each
were collected during the ground campaign.Several dominant crop calendars were also recorded as ancillary information for image interpretation.The preprocessing of ASAR images includes the calibration
speckle noise filtering
co-registration
and geo-reference.Backscattering coefficients of different crops and other land surface objects were obtained from ASAR images overlaid with the GPS samples and all were averaged for further analysis.Before the rice mapping
the backscattering difference of land surface objects was analyzed by the maps of multi-polarization backscattering difference for each date and multi-temporal backscattering difference of the same polarization which were calculated from the received ASAR data.Accordingly
paddy rice showed significant disparity of backscattering coefficient between VV polarization and HH polarization
while the multi-polarization backscattering difference was not obvious for other land surface objects.Moreover
high sensitivity of HH polarization radar wave to the temporal change of paddy rice was observed
which indicated multi-temporal backscattering difference of HH polarization contained more paddy rice information.However
it should be that the ASAR images employed in the above analysis were acquired at different rice growth stage one of which is at rice transplanting stage and the other at booting stage.During the rice transplanting
the dominant scattering mechanism is the surface-volume scattering
while single-volume scattering becomes the dominant scattering mechanism during the booting stage since paddy canopy has become much denser.Therefore
an optimal combination of backscattering difference images used for paddy rice mapping was obtained.Then
two classification methods are compared: threshold classification and supervised classification were applied separately to retrieve paddy rice from the combined image.As a result
a high rice identification accuracy of 84.92% was achieved using the supervised classification.The relative inferior performance of threshold classification could be caused by the remained speckle noise
because the threshold classification was performed at pixel scale.Finally
based on the multi-remporal and multi-polarization backscattering difference
the practical approach presented in this study not only simplifies traditional rice mapping methods
but also keeps a high mapping accuracy of rice crop.However
limited by the available ASAR data
further study should be extended for investigation of ASAR data on rice mapping of different rice calendar and for rice yield estimation using multi-temporal and multi-polarization radar images.
水稻制图影像分类ASAR
rice mappingimage classificationASAR
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