предыдущий|следующий

Cotton extraction method of integrated multi-features based on multi-temporal Landsat 8 images

  • role： First author第一作者
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

  • Email：wangwenjlkfl@126.com
  • Introduction：王文静(1989—    )，女，硕士研究生，研究方向为遥感分类及信息提取。E-mail：wangwenjlkfl@126.com
WANG Wenjing12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhangxia@radi.ac.cn
  • Introduction：张霞(1972—    )，女，研究员，研究方向为光谱响应机理、图像处理与信息提取。E-mail：zhangxia@radi.ac.cn
ZHANG Xia1*,  
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

ZHAO Yindi2,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

WANG Shudong1

реферат

Cotton is a significant economic crop, and cotton extraction plays an important role in effective and controllable agricultural management. Multi-temporal remote sensing images have been widely used in cotton extraction, but these studies mainly focused on sole features, such as the Normalized Difference Vegetation Index (NDVI). An effective method of integrated multi-features based on multi-temporal Landsat 8 images was proposed to extract cotton information.In this study, we chose north-central Shawan County in Xinjiang Uygur Autonomous Region as the study area. Nine images taken by Landsat 8 in 2013 were collected for cotton extraction. NDVI time series were generated to characterize the phenological pattern of each land cover type. The optimal temporal reflectance image was selectedby analyzing the difference in NDVI profile between cotton and other crop types. Texture features were calculated by the gray-level co-occurrence matrix method. NDVI time series, optimal temporal reflectance image, and texture features were combined as the original classification features. When the training samples were sufficient,butthe featureswereexcessive, the classification accuracy may decrease because of redundant information. We completed feature selection by using the rough set method and then obtained the selective features of the original features. The NDVI time series, original features, and selective features were used for classification by the support vector machine. The cotton distribution map was generated based on the classification result of the highest accuracy. Finally, we evaluated the accuracy of classification results by confusion matrix.①The selection of the optimal temporal reflectance image for cotton identification is important, and the optimum phase of this studyis on September 4. In this period, wheat was harvested; corn and sunflower were in the mature period; andcotton was in the blossom period. Significant differences were observed among these crops in the optimum phase. ②The original features achieved accuracies of 87.4% and 87.93% for cotton producers and users, respectively, and the overall accuracy was 92.81%. Compared with the classification results of the NDVI time series, the overall accuracy increased by 5.53% and the accuracy of cotton producers increased by 5.05%.Moreover, the classification accuracies of other land cover types increased to varying extents. ③The selective features achieved accuracies of 92.73% and 90.36% for cotton producers and users, respectively, and the overall accuracy was 93.66%. Compared with the classification results of the original features, the overall accuracy increased by 0.85% and the accuracy of cotton producers increased by 5.33%.Experiments showed that feature selection by the rough set method not only improved the classification accuracy but also effectively reduced the classification complexity. The proposed method achieved an accuracy of 92.73% for cotton extraction. The method of integrated multi-features based on multi-temporal Landsat 8 images is promising for crop extraction, even for land cover classification.

ключеви́че слова́

multi-temporal Landsat 8 images;integrated multi-features;NDVI;cotton extraction;feature selection

References

  1. 1.
    Brown J C, Kastens J H, Coutinho A C, de Castro Victoria D and Bishop C R. 2013. Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sensing of Environment, 130: 39–50
  2. 2.
    Cao W B, Yang B J and Song J P. 2004. Spectral information based model for cotton identification on Landsat TM image. Transactions of the CSAE, 20(4): 112–116
  3. 3.
    Conese C and Maselli F. 1991. Use of multitemporal information to improve classification performance of TM scenes in complex terrain. ISPRS Journal of Photogrammetry and Remote Sensing, 46(4): 187–197
  4. 4.
    Gu Y F and Zhang Y. 2003. Feature extraction based on automatic subspace partition for hyperspectral images. Remote Sensing Technology and Application, 18(6): 384–387
  5. 5.
    Haralick R M, Shanmugam K and Dinstein I H. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6): 610–621
  6. 6.
    Hlavka C A, Haralick R M, Carlyle S M and Yokoyama R. 1980. The discrimination of winter wheat using a growth-state signature. Remote Sensing of Environment, 9(4): 277–294
  7. 7.
    Liu J K, Zhong S Q and Liang W H. 2015. Extraction on crops planting structure based on multi-temporal Landsat 8 OLI images. Remote Sensing Technology and Application, 30(4): 775–783
  8. 8.
    Liu M C, Li X, Jiang P A and Sheng J D. 2005. The application of ASTER data in cotton information classification-taking 16 farm of Nongyishi of production and construction group as an example. Remote Sensing Technology and Application, 20(6): 591–595
  9. 9.
    Oguro Y, Suga Y, Takeuchi S, Ogawal H and Tsuchiya K. 2003. Monitoring of a rice field using Landsat-5 TM and Landsat-7 ETM+ data. Advances in Space Research, 32(11): 2223–2228
  10. 10.
    Pan Y, Yang J H and Wu W B. 2012. Neural network based on rough sets reduction and its application to remote sensing image classification. Remote Sensing Information, 27(4): 86–90
  11. 11.
    Thangavel K and Pethalakshmi A. 2009. Dimensionality reduction based on rough set theory: a review. Applied Soft Computing, 9(1): 1–12
  12. 12.
    Vintrou E, Desbrosse A, Bégué A, Traoré S, Baron C and Lo Seen D. 2012. Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products. International Journal of Applied Earth Observation and Geoinformation, 14(1): 83–93
  13. 13.
    Wang G Y, Yao Y Y and Yu H. 2009. A survey on rough set theory and applications. Chinese Journal of Computers, 32(7): 1229–1246
  14. 14.
    Wu H, Li S J, Lin L and Wan D S. 2010. Multiple-strategy combination based approach to band selection for hyper-spectral image classification. Journal of Frontiers of Computer Science and Technology, 4(5): 464–472
  15. 15.
    许新征. 2012. 基于粗糙集的粒度神经网络研究. 北京: 中国矿业大学Xu X Z. 2012. Study on Granular Neural Networks based on Rough Sets. Beijing: China University of Mining and Technology
  16. 16.
    Yin J H and Wang Y S. 2010. An algorithm of rapid optimum band selection from hyperspectral remote sensing image. Remote Sensing Information, 25(3): 3–6, 12
  17. 17.
    Zhao C H, Chen W H and Yang L. 2007. Research advances and analysis of hyperspectral remote sensing image band selection. Journal of Natural Science of Heilongjiang University, 24(5): 592–602
  18. 18.
    郑长春. 2008. 水稻种植面积遥感信息提取研究. 乌鲁木齐: 新疆农业大学[DOI: 10.7666/d.y1256827]Zheng C C. 2008. Study on Remote Sensing Information Extraction of Paddy Rice Planting Area. Urumqi: Xinjiang Agriculture University
  19. 19.
    Zhu L, Ping B, Su F Z, Du Y Y and Su W G. 2013. Using decision tree model to extract paddy rice information from multi-temporal TM images. Journal of Geo-information Science, 15(3): 446–451

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website