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利用SAR影像时间序列的耕地提取研究

DOI: 10.18306/dlkxjz.2015.07.005, PP. 830-839

Keywords: 时间序列,合成孔径雷达,耕地,一致性,变异系数,相关系数,动态时间弯曲

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Abstract:

卫星遥感是耕地资源调查的一种重要技术手段,利用遥感时间序列数据进行耕地提取具有很强的实践意义。光学遥感成像过程易受光照和大气条件影响,在云雨多发地区所能获取的可用数据十分有限;合成孔径雷达(SAR)能够全天时、全天气进行对地观测,但受斑点噪声影响,少见利用其构建时间序列进行信息提取的研究。本文研究了SAR影像时间序列在耕地提取中的适用性,利用江苏省徐州市2009年12月-2010年12月共11景ENVISATASAR影像构建时间序列,目视选取30个5像元×5像元大小的耕地样区,分别统计样区内(相邻位置)与样区间(不同位置)耕地时域后向散射特征的一致性(变异系数);然后利用欧氏距离法、相关系数法以及动态时间弯曲法(DTW)进行研究区的耕地提取。结果显示相邻位置耕地像元后向散射特性较为一致,平均变异系数为9.96%;不同位置耕地像元后向散射特性一致性也较好,平均变异系数为15.27%。在所选的3种方法中,相关系数法耕地提取精度最高,正确率与完整率分别为86.25%与80.70%;欧氏距离法精度次之,正确率与完整率分别为76.40%与71.93%;DTW效果较差,正确率和完整率分别为62.15%和77.78%。SAR影像时间序列作为一种新的数据组织形式,可用于耕地的有效提取。

References

[1]  1 陈红, 吴世新, 冯雪力. 2010. 新疆耕地时空变化特征[J]. 地理科学进展, 29(3): 313-318. [Chen H, Wu S X, Feng X L. 2010. Research of changes in cultivated land in Xinjiang based on RS and GIS[J]. Progress in Geography, 29(3): 313-318.]
[2]  2 侯光雷, 张洪岩, 王野乔, 等. 2010. 基于时间序列谐波分析的东北地区耕地资源提取[J].自然资源学报, 25(9): 1607-1617. [Hou G L, Zhang H Y, Wang Y Q, et al.2010. Application of harmonic analysis of time series to extracting the crop land resource in Northeast China[J]. Journal of Natural Resources, 25(9): 1607-1617.]
[3]  3 李海林, 杨丽彬. 2013. 基于增量动态时间弯曲的时间序列相似性度量方法[J]. 计算机科学, 40(4): 227-230. [Li H L, Yang L B. 2013. Similarity measure for time series based on incremental dynamic time warping[J]. Computer Science, 40(4): 227-230.]
[4]  4 孙越凡, 钟礼山, 程亮, 等. 2014. 动态时间弯曲技术支持下时序NDVI数据的耕地分布信息提取[J]. 资源科学, 36(9): 1977-1984. [Sun Y F, Zhong L S, Cheng L, et al.2014. Cropland information extraction from time series NDVI data using dynamic time warp[J]. Resources Science, 36(9): 1977-1984.]
[5]  5 孙增国, 韩崇昭. 2010. 基于区域分类、自适应滑动窗和结构检测的合成孔径雷达图像联合降斑算法[J]. 物理学报, 59(5): 3210-3220. [Sun Z G, Han C Z. 2010. Combined despeckling algorithm of synthetic aperture radar images based on region classification, adaptive windowing and structure dectection[J]. Acta Physica Sinica, 59(5): 3210-3220.]
[6]  6 王亚飞, 程亮, 钟礼山, 等. 2013. 像素级SAR影像时间序列的建模方法研究[J]. 地理与地理信息科学, 29(4): 109-112. [Wang Y F, Cheng L, Zhong L S, et al.2013. Research on modeling method for pixel-level SAR image time series[J]. Geography and Geo-Information Science, 29(4): 109-112.]
[7]  7 杨忍, 刘彦随, 陈玉福, 等. 2013. 环渤海地区耕地复种指数时空变化遥感反演及影响因素探测[J]. 地理科学, 33(5): 588-593. [Yang R, Liu Y S, Chen Y F, et al.2013. The remote sensing Inversion for spatial and temporal changes of multiple cropping index and detection for influencing factors around Bohai Rim in China[J]. Scientia Geographica Sinica, 33(5): 588-593.]
[8]  8 Balenzano A, Mattia F, Satalino G, et al.2011. Dense temporal series of C- and L-band SAR data for soil moisture retrieval over agricultural crops[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2): 439-450.
[9]  9 Bouvet A, Toan L T, Lam-Dao N. 2009. Monitoring of the rice cropping system in the Mekong Delta using ENVISAT/ASAR dual polarization data[J]. IEEE Transactions on Geoscience and Remote Sensing, 47(2): 517-526.
[10]  10 Chakraborty M, Manjunath K R, Panigrahy S, et al.2005. Rice crop parameter retrieval using multi-temporal, multi-incidence angle radarsat SAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 59(5): 310-322.
[11]  11 Cheng L, Wang Y F, Li M C, et al.2014. Generation of pixel-level SAR image time series using a locally adaptive matching technique[J]. Photogrammetric Engineering and Remote Sensing, 80(9): 839-848.
[12]  12 Hoshikawa K, Nagano T, Kotera A, et al.2014 Classification of crop fields in northeast Thailand based on hydrological characteristics detected by L-band SAR backscatter data[J]. Remote Sensing Letters, 5(4): 323-331.
[13]  13 Jakubauskas M E, Legates D R, Kastens J H. 2002. Crop identification using harmonic analysis of time-series AVHRR NDVI data[J]. Computers and Electronics in Agriculture, 37(1-3): 127-139.
[14]  14 Knight J F, Lunetta R S, Ediriwickrema J, et al.2006. Regional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: a phenology-based approach[J]. Giscience & Remote Sensing, 43(1): 1-23.
[15]  15 Petitjean F, Inglada J, Gancarski P. 2012. Satellite image time series analysis under time warping[J]. IEEE Transactions on Geoscience and Remote Sensing, 50(8): 3081-3095.
[16]  16 Rosich B, Meadows P. 2004. Absolute calibration of ASAR level 1 products[Z/OL]. 2004-10-07[2014-12-. https://earth.esa.int/web/guest/-/absolute-calibration-of-asar-level-1-products-generated-with-pf-asar-4503.
[17]  17 Sakamoto T, Yokozawa M, Toritani H, et al.2005. A crop phenology detection method using time-series MODIS data[J]. Remote Sensing of Environment, 96(3-4): 366-374.
[18]  18 Skriver H, Mattia F, Satalino G, et al.2011. Crop classification using short-revisit multitemporal SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2): 423-431.
[19]  19 Small D, Schubert A. 2008. Guide to ASAR geocoding[Z/OL]. 2008-04-30[2014-12-. http://www.geo.uzh.ch/microsite/rsl-documents/research/publications/other-sci-communications/2008_RSL-ASAR-GC-AD-v101-0335607552/2008_RSL-ASAR-GC-AD-v101.pdf.
[20]  20 Tan C P, Ewe H T, Chuah H T. 2011. Agricultural crop-type classification of multi-polarization SAR images using a hybrid entropy decomposition and support vector machine technique[J]. International Journal of Remote Sensing, 32(22): 7057-7071.
[21]  21 Victoria D D, Da Paz A R, Coutinho A C, et al.2012. Cropland area estimates using MODIS NDVI time series in the state of Mato Grosso, Brazil[J]. Pesquisa Agropecuaria Brasileira, 47(9): 1270-1278.
[22]  22 Wardlow B D, Egbert S L. 2008. Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the US central great plains[J]. Remote Sensing of Environment, 112(3): 1096-1116.
[23]  23 Wu F, Wang C, Zhang H, et al.2011. Rice crop monitoring in South China with RADARSAT-2 quad-polarization SAR data[J]. IEEE Geoscience and Remote Sensing Letters, 8(2): 196-200.

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