浏览全部资源
扫码关注微信
Received：08 August 2019，
Published：07 October 2020
移动端阅览
红边作为植被敏感波段，其红边特征的运用是遥感识别农作物并实现精准农业的高新手段之一。以黑龙江松嫩平原北部为研究区，以国内首个提供红边波段的多光谱高分六号影像和玉米、大豆、水稻总计82859个作物样本同时作为研究对象，从以下几个方面研究了红边波段和红边指数波段等红边特征在农作物识别中的表现，并评估了农作物的识别精度。（1） 通过作物样本辐射亮度值的统计特征，初步显示了在两红边波段0.710 μm和0.750 μm处有比其他波段更好的区分；（2） 根据传统归一化植被指数形式构建了红边归一化植被指数NDVI710和NDVI750，综合两指数在J-M距离表征的作物样本类别区分度上比传统NDVI更显著；（3） 通过多种手段筛选了有效波段并且制定了支持向量机（SVM）框架下4种农作物识别的分类策略，分别在5∶5、6∶4、7∶3、8∶2、9∶1等5套随机样本分割方案下完成研究区域农作物的分类预测。在这20类分类精度中kappa系数均高于0.9609，总体精度高于0.9742；列向上5∶5分割方案的精度最高，8∶2的精度最低；横向上分类精度排序如下：SVM-RFE > SVM-RF > SVM-有红边波段 > SVM-无红边波段，该结果表明了红边指数和红边波段的参与显著地提高了作物的识别精度；（4） 由于水域等其他样本的缺少，SVM-RFE方法和SVM-RF方法的分类图像均存在少量错分现象。但从分类精度和图像细节展示上来看，SVM-RFE方法要优于SVM-RF方法，二者分类图像的交叉验证中kappa系数为0.8060，总体精度为0.8743。总之，高分六号红边特征在作物识别中表现优越，使得识别精度显著提高。后续研究者可开发更多与红边相关的植被指数，充分发挥红边特征在精准农业中的作用。
The application of red edge features
which are sensitive bands of vegetation
is a high-technology method for remote sensing to identify crops and realize precision agriculture. Multispectral GF-6 image of the study area in the northern region of Songnen Plain in Heilongjiang Province pioneers the use red edge bands in China. A total of 82859 crop samples of corn
soybean
and rice were used as research objects. The classification accuracy of crops was evaluated and the performance of red edge features in crop identification
such as red edge bands and vegetation index
was discussed from the following aspects. (1) Statistical characteristics of radiance values of crop samples initially showed that discrimination is better at Band 5-0.710 μm and Band 6-0.750 μm in the two red edge bands than findings of other GF-6 bands. (2) Traditional normalized (NDVI) and red-edge normalized difference vegetation indexes
namely
NDVI
710
and NDVI
750
are constructed. Results showed that the two indexes are more significant than the traditional NDVI in the classification of crop samples characterized using J-M distance. (3) Effective bands are screened using various methods
and classification strategies for the four types of crops are formulated using Support Vector Machine (SVM). Crop classification in the study area is completed using five sets of random sample segmentation schemes
namely
5∶5
6∶4
7∶3
8∶2
and 9∶1. Twenty types of classification accuracy demonstrated a kappa coefficient higher than 0.9609 and overall accuracy higher than 0.9742. The 5∶5 and 8∶2 segmentation schemes in the column direction exhibited the highest and lowest accuracy
respectively. The sorting accuracy in the horizontal direction demonstrated the following order: SVM-RFE
>
SVM-RF
>
SVM with red edge bands
>
SVM without red edge band
which also showed that the participation of red edge vegetation index and red edge band significantly improves the recognition precision of crops. (4) SVM-RFE and SVM-RF both obtained minimal misclassifications due to the lack of other samples
such as waters. However
SVM-RFE is superior to SVM-RF in terms of classification accuracy and image detail display with a kappa coefficient and overall accuracy of 0.8060 and 0.8743
respectively
in the cross-validation of two classified images. Hence
the red edge feature of GF-6 is superior in crop recognition with its significantly improved recognition accuracy. Subsequent investigations can focus on developing additional red edge-related vegetation indexes and optimize the role of red edge characteristics in precision agriculture.
Bödinger C J . 2018 . Remote Sensing of Vegetation: Along a Latitudinal Gradient in Chile . Wiesbaden : Springer : 123 [ DOI: 10.1007/978-3-658-25120-8 http://dx.doi.org/10.1007/978-3-658-25120-8 ]
Danson F M and Plummer S E . 1995 . Red-edge response to forest leaf area index . International Journal of Remote Sensing , 16 ( 1 ): 183 - 188 [ DOI: 10.1080/01431169508954387 http://dx.doi.org/10.1080/01431169508954387 ]
Deng S B , Chen Q J , Du H J and Xu E H . 2014 . Remote Sensing Image Processing Method Using ENVI . 2nd ed . Beijing : Higher Education Press : 474
邓书斌 , 陈秋锦 , 杜会建 , 徐恩惠 . 2014 . ENVI遥感图像处理方法 . 2版 . 北京 : 高等教育出版社 : 474
ENVI-IDL Technology Hall . 2019 . Atmospheric correction method for WFV data of Gaofen-6 Satellite in ENVI [EB/OL]. ( 2019-07-12 )[ 2019-08-06 ]. http://blog.sina.com.cn/s/blog_764b1e9d0102zurl.html (ENVI-IDL http://blog.sina.com.cn/s/blog_764b1e9d0102zurl.html(ENVI-IDL
技术殿堂 . 2019 . ENVI中高分六号WFV数据大气校正方法 [EB/OL]. ( 2019-07-12 )[ 2019-08-06 ]. http://blog.sina.com.cn/s/blog_764b1e9d0102zurl.html http://blog.sina.com.cn/s/blog_764b1e9d0102zurl.html
ESA . 2008 . RapidEye [EB/OL]. [ 2019-08-06 ]. https://earth.esa.int/web/guest/missions/3rd-party-missions/current-missions/rapideye https://earth.esa.int/web/guest/missions/3rd-party-missions/current-missions/rapideye
ESA . 2015 . Sentinel-2 [EB/OL]. [ 2019-08-06 ]. https://sentinel.esa.int/web/sentinel/missions/sentinel-2 https://sentinel.esa.int/web/sentinel/missions/sentinel-2
Filella I and Penuelas J . 1994 . The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status . International Journal of Remote Sensing , 15 ( 7 ): 1459 - 1470 [ DOI: 10.1080/01431169408954177 http://dx.doi.org/10.1080/01431169408954177 ]
Foster A J , Kakani V G , Ge J J , Gregory M and Mosali J . 2016 . Discriminant analysis of nitrogen treatments in switchgrass and high biomass sorghum using leaf and canopy-scale reflectance spectroscopy . International Journal of Remote Sensing , 37 ( 10 ): 2252 - 2279 [ DOI: 10.1080/01431161.2016.1171926 http://dx.doi.org/10.1080/01431161.2016.1171926 ]
Fu H and Xu G S . 2014 . the research of combined feature selection based on random forest and RFE // Proceedings of the 19th National Youth Communication Academic Conference . Shanghai : China Institute of Communications : 200 - 204
傅昊 , 徐国胜 . 2014 . 基于随机森林和RFE的组合特征选择的研究 // 第十九届全国青年通信学术年会论文集 . 上海 : 中国通信学会 : 200 - 204
Géron A , 2017 . Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems . Beijing: O'Reilly Media: 450
Huang B , Wu B , Liu B and Cao K . 2008 . Spatial intelligence : advancement of geographic information science. Journal of Remote Sensing , 12 ( 5 ): 766 - 771
黄波 , 吴波 , 刘彪 , 曹凯 . 2008 . 空间智能: 地理信息科学的新进展 . 遥感学报 , 12 ( 5 ): 766 - 771 [ DOI: 10.11834/jrs.200805100 http://dx.doi.org/10.11834/jrs.200805100 ]
Huang S Y , Yang L , Chen X and Yao Y . 2018 . Study of typical arid crops classification based on machine learning . Spectroscopy and Spectral Analysis , 38 ( 10 ): 3169 - 3176
黄双燕 , 杨辽 , 陈曦 , 姚远 . 2018 . 机器学习法的干旱区典型农作物分类 . 光谱学与光谱分析 , 38 ( 10 ): 3169 - 3176 ) [ DOI: 10.3964/j.issn.1000-0593(201810-3169-08 http://dx.doi.org/10.3964/j.issn.1000-0593(2018)10-3169-08 ]
Jiang J B , Chen Y H and Huang W J . 2010 . Using the distance between hyperspectral red edge position and yellow edge position to identify wheat yellow rust disease . Spectroscopy and Spectral Analysis , 38 ( 10 ): 1614 - 1618
蒋金豹 , 陈云浩 , 黄文江 . 2010 . 利用高光谱红边与黄边位置距离识别小麦条锈病 . 光谱学与光谱分析 , 30 ( 6 ): 1614 - 1618 ) [ DOI: 10.3964/j.issn.1000-0593(201006-1614-05 http://dx.doi.org/10.3964/j.issn.1000-0593(2010)06-1614-05 ]
Lamb D W , Steyn-Ross M , Schaare P , Hanna M M , Silvester W and Steyn-Ross A . 2002 . Estimating leaf nitrogen concentration in ryegrass ( Lolium spp.) pasture using the chlorophyll red-edge: theoretical modelling and experimental observations . International Journal of Remote Sensing , 23 ( 18 ): 3619 - 3648 [ DOI: 10.1080/01431160110114529 http://dx.doi.org/10.1080/01431160110114529 ]
Li X C . 2018 . Starting with Lasso [EB/OL]. ( 2018-10-22 )[ 2019-08-06 ]. https://zhuanlan.zhihu.com/p/46999826 ( https://zhuanlan.zhihu.com/p/46999826(
李新春 . 2018 . 从Lasso开始说起 [EB/OL]. ( 2018-10-22 )[ 2019-08-06 ]. https://zhuanlan.zhihu.com/p/46999826 https://zhuanlan.zhihu.com/p/46999826
Liu J , Wang L M , Yang F G , Yao B M and Yang L B . 2018 . Recognition ability of red edge and short wave infrared spectrum on maize and soybean . Chinese Agricultural Science Bulletin , 34 ( 35 ): 120 - 129
刘佳 , 王利民 , 杨福刚 , 姚保民 , 杨玲波 . 2018 . 红边与短波红外谱段的玉米大豆识别能力研究 . 中国农学通报 , 34 ( 35 ): 120 - 129
Liu L Y , Wang J H , Huang W J , Zhao C J , Zhang B and Tong Q X . 2004 . Estimating winter wheat plant water content using red edge parameters . International Journal of Remote Sensing , 25 ( 17 ): 3331 - 3342 [ DOI: 10.1080/01431160310001654365 http://dx.doi.org/10.1080/01431160310001654365 ]
Luo G . 2018 . Perceiving the Earth - Questions and Answers on Satellite Remote Sensing Knowledge . Beijing : China Aerospace Publishing House : 381
罗格 . 2018 . 感知地球-卫星遥感知识问答 . 北京 : 中国宇航出版社 : 381
Milton N M , Eiswerth B A and Ager C M . 1991 . Effect of phosphorus deficiency on spectral reflectance and morphology of soybean plants . Remote Sensing of Environment , 36 ( 2 ): 121 - 127 [ DOI: 10.1016/0034-4257(91)90034-4 http://dx.doi.org/10.1016/0034-4257(91)90034-4 ]
Mutanga O and Kumar L . 2007 . Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data . International Journal of Remote Sensing , 28 ( 21 ): 4897 - 4911 [ DOI: 10.1080/01431160701253253 http://dx.doi.org/10.1080/01431160701253253 ]
Reichstein M , Camps-Valls G , Stevens B , Jung M , Denzler J , Carvalhais N and Prabhat . 2019 . Deep learning and process understanding for data-driven Earth system science . Nature , 566 ( 7743 ): 195 - 204 [ DOI: 10.1038/s41586-019-0912-1 http://dx.doi.org/10.1038/s41586-019-0912-1 ]
Satellite Imaging Corporation . 2017 . WorldView-3 satellite sensor [EB/OL]. [ 2019-08-06 ]. https://www.satimagingcorp.com/satellite-sensors/worldview-3/ https://www.satimagingcorp.com/satellite-sensors/worldview-3/
Strobl C , Boulesteix A L , Kneib T , Augustin T and Zeileis A . 2008 . Conditional variable importance for random forests . BMC Bioinformatics , 9 ( 1 ): 307 [ DOI: 10.1186/1471-2105-9-307 http://dx.doi.org/10.1186/1471-2105-9-307 ]
Thenkabail P S , Lyon J G and Huete A . 2019 . Hyperspectral Remote Sensing of Vegetation, Volume II: Hyperspectral Indices and Image Classifications for Agriculture and Vegetation. 2nd ed. Boca Raton, FL: CRC Press, Taylor & Francis Group: 333
Tian Z K , Fu Y Y and Liu S H . 2019 . Remote sensing image classification based on heterogeneous machine learning algorithm fusion . Computer Science , 46 ( 5 ): 235 - 240
田振坤 , 傅莺莺 , 刘素红 . 2019 . 基于异构机器学习算法融合的遥感影像分类 . 计算机科学 , 46 ( 5 ): 235 - 240 [ DOI: 10.11896/j.issn. 1002-137X.2019.05.036 http://dx.doi.org/10.11896/j.issn.1002-137X.2019.05.036 ]
Wang Y Y , Chen Y H , Li J and Huang W J . 2007 . Two new red edge indices as indicators for stripe rust disease severity of winter wheat . Journal of Remote Sensing , 11 ( 6 ): 875 - 881
王圆圆 , 陈云浩 , 李京 , 黄文江 . 2007 . 指示冬小麦条锈病严重度的两个新的红边参数 . 遥感学报 , 11 ( 6 ): 875 - 881 [ DOI: 10.11834/jrs.200706118 http://dx.doi.org/10.11834/jrs.200706118 ]
Wu J Y , Yang X D , Zhang F J , Ni J , Tian W X and Xie L Y . 1997 . Seasonal characteristics of spectral reflectance of Korean pine leaves in the gold mine area of Zhaoyuan City in Shandong province . Journal of Remote Sensing , 1 ( 2 ): 124 - 128
吴继友 , 杨旭东 , 张福君 , 倪健 , 田文新 , 解立业 . 山东招远金矿区赤松针叶反射光谱红边的季节特征 . 遥感学报 , 1 ( 2 ): 124 - 128 [ DOI: 10.11834/jrs.19970208 http://dx.doi.org/10.11834/jrs.19970208 ]
Wu Y F , Hu X , Lü G H , Ren D C , Jiang W G and Song J Q . 2014 . Comparison of red edge parameters of winter wheat canopy under late frost stress . Spectroscopy and Spectral Analysis , 38 ( 10 ): 2190 - 2195
武永峰 , 胡新 , 吕国华 , 任德超 , 蒋卫国 , 宋吉青 . 2014 . 晚霜冻影响下冬小麦冠层红边参数比较 . 光谱学与光谱分析 , 34 ( 8 ): 2190 - 2195 ) [ DOI: 10.3964/j.issn.1000-0593(201408-2190-06 http://dx.doi.org/10.3964/j.issn.1000-0593(2014)08-2190-06 ]
Wuyundeji , Yu L F , Bao J W , Xu H T and Wulantuya . 2017 . Impact of red-edge waveband of RapidEye satellite on recognition ability of main crop . Journal of Northern Agriculture , 45 ( 6 ): 118 - 123
乌云德吉 , 于利峰 , 包珺玮 , 许洪滔 , 乌兰吐雅 . 2017 . RapidEye卫星红边波段对主要农作物识别能力的影响研究 . 北方农业学报 , 45 ( 6 ): 118 - 123 [ DOI: 10.3969/j.issn.2096-1197.2017.06.22 http://dx.doi.org/10.3969/j.issn.2096-1197.2017.06.22 ]
Xie Q Y . 2017 . Research on Leaf Area Index Retrieve Methods Based on the Red Edge Bands from Multi-Platform Remote Sensing Data . Beijing :
University of Chinese Academy of Sciences (Institute of Remote Sensing and Digital Earth , Chinese Academy of Sciences 谢巧云 . 2017 . 考虑红边特性的多平台遥感数据叶面积指数反演方法研究 . 北京 : 中国科学院大学(中国科学院遥感与数字地球研究所))
Yuan M Y . 2018 . the Elements of Machine Learning: Principles Algorithms and Practices . Beijing : Tsinghua University Press : 304 (袁梅宇 . 2018 . 机器学习基础—原理、算法与实践 . 北京 : 清华大学出版社 : 304 )
Zhao C J , Wang Z J , Wang J H and Huang W J . 2012 . Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat ( Triticum aestivum L .) spectra. International Journal of Remote Sensing , 33 ( 11 ): 3472 - 3491 [ DOI: 10.1080/01431161.2011.604052 http://dx.doi.org/10.1080/01431161.2011.604052 ]
Zhao D L , Li J H and Song Z J . 2003 . Hyperspectral remote sensing for estimating biochemical variables of canopy . Advance in Earth Sciences , 18 ( 1 ): 94 - 99
赵德龙 , 李建华 , 宋子健 . 2003 . 高光谱技术提取植被生化参数机理与方法研究进展 . 地球科学进展 , 18 ( 1 ): 94-99 [ DOI: 10.3321/j.issn:1001-8166.2003.01.013 http://dx.doi.org/10.3321/j.issn:1001-8166.2003.01.013 ]
Zhao Y S . 2013 . Principle and Method of Remote Sensing Application Analysis . 2nd ed . Beijing : Science Press : 502
赵英时 . 2013 . 遥感应用分析原理与方法 . 2版 . 北京 : 科学出版社 : 502
Zheng L J . 2017 . Crop Classification Using Multi-features of Chinese Gaofen- 1 / 6 Satellite Remote Sensing Images .
Beijing : University of Chinese Academy of Sciences (Institute of Remote Sensing and Digital Earth , Chinese Academy of Sciences 郑利娟 . 2017 . 基于高分一/六号卫星影像特征的农作物分类研究 . 北京 : 中国科学院大学(中国科学院遥感与数字地球研究所))
Zheng Y , Wu B F and Zhang M . 2017 . Estimating the above ground biomass of winter wheat using the Sentinel-2 data . Journal of Remote Sensing , 21 ( 2 ): 318 - 328
郑阳 , 吴炳方 , 张淼 . 2017 . Sentinel-2数据的冬小麦地上干生物量估算及评价 . 遥感学报 , 21 ( 2 ): 318 - 328 [ DOI: 10.11834/jrs.20176269 http://dx.doi.org/10.11834/jrs.20176269 ]
Zhou X , Wang Y B , Liu S H , Yu P X and Wang X K . 2018 . A machine learning algorithm for automatic identification of cultivated land in remote sensing images . Remote sensing for Land and Resources , 30 ( 4 ): 68 - 73
周询 , 王跃宾 , 刘素红 , 于佩鑫 , 王西凯 . 2018 . 一种遥感影像自动识别耕地类型的机器学习算法 . 国土资源遥感 , 30 ( 4 ): 68-73 [ DOI: 10.6046/gtzyyg.2018.04.11 http://dx.doi.org/10.6046/gtzyyg.2018.04.11 ]
Zhou Z H . 2016 . Machine Learning . Beijing : Tsinghua University Press : 425
周志华 . 2016 . 机器学习 . 北京 : 清华大学出版社 : 425
Zhu Y Q , Qu Y H , Liu S H and Chen S B . 2014 . Spectral response of wheat and lettuce to copper pollution . Journal of Remote Sensing , 18 ( 2 ): 335 - 352
朱叶青 , 屈永华 , 刘素红 , 陈圣波 . 2014 . 重金属铜污染植被光谱响应特征研究 . 遥感学报 , 18 ( 2 ): 335 - 352 [ DOI: 10.11834/jrs.20143073 http://dx.doi.org/10.11834/jrs.20143073 ]
相关文章
相关作者
相关机构
京公网安备11010802024621