利用SAR影像时间序列的耕地提取研究

doi:10.18306/dlkxjz.2015.07.005

摘要/Abstract

摘要：

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ENVISAT ASAR 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。

关键词: 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

Abstract:

Satellite remote sensing is an important technique for cropland resources survey, while time series of remote sensing images, particularly, are of great practical significance for cropland extraction. Optical remote sensing imagery is largely affected by illumination and atmospheric conditions, which limits available satellite images within a year, especially where cloudy or rainy weather frequently occurs. Synthetic Aperture Radar (SAR), on the other hand, is able to acquire data throughout the day under any weather condition. However, owing to the influence of speckle noise, very little work has been done to use SAR image time series for feature extraction. This study examines the applicability of SAR image time series for cropland extraction, and Xuzhou City in Jiangsu Province was chosen as the study area. A total of 11 ENVISAT ASAR images covering the study area and dated from December 2009 to December 2010 were selected to establish a SAR time series as experimental data. Thirty cropland sampling regions with the size of 5 pixels × 5 pixels were visually chosen to calculate the consistency of cropland backscatter signatures in the temporal domain, at both neighboring location (inside each sampling region) and remote location (beyond the sampling regions). Euclidean distance method, correlation method, and dynamic time warp (DTW) method were then adopted to extract cropland pixels in the study area. The experiment results show high backscattering consistency for neighboring cropland pixels, with a coefficient of variation of 9.96%. A lower but still satisfactory backscattering consistency was derived by remote cropland pixels in the study area, with a coefficient of variation of 15.27%. Despite the inherent speckle noises of SAR data, the general characteristics of time series for cropland backscatter coefficient correspond well with crop calendar. For the three selected methods, correlation method performed best, which produced a correctness of 86.25% and completeness of 80.70%. Euclidean method took the second place, with a correctness of 76.40% and a completeness of 71.93%. DTW achieved the lowest accuracy, with a correctness of 62.15% and completeness of 77.78%. This research shows that as a new data organizing form, time series of SAR images can be used for cropland extraction effectively.

Key words: time series, SAR, cropland, consistency, coefficient of variation, correlation coefficient, DTW

钟礼山, 李满春, 伍阳, 夏南, 程亮. 利用SAR影像时间序列的耕地提取研究[J]. 地理科学进展, 2015, 34(7): 830-839.

Lishan ZHONG, Manchun LI, Yang WU, Nan XIA, Liang CHENG. Cropland extraction using SAR time series image[J]. PROGRESS IN GEOGRAPHY, 2015, 34(7): 830-839.

图/表 7

图1

图2

图3

图4

图5

图6

表1

参考文献 23

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	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	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	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	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	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	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	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	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	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	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	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	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	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	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	Rosich B, Meadows P.2004. Absolute calibration of ASAR level 1 products[Z/OL]. 2004-10-07[2014-12-04]. https://earth.esa.int/web/guest/-/absolute-calibration-of-asar-level-1-products-generated-with-pf-asar-4503.
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	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	Small D, Schubert A. 2008. Guide to ASAR geocoding[Z/OL]. 2008-04-30[2014-12-04]. .
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	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	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	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.

[1]	徐小任, 王梁, 徐勇, 段健. 基于“三类”空间的后备可利用土地资源评价研究——以山西省介休市为例[J]. 地理科学进展, 2021, 40(2): 272-282.
[2]	王亚辉, 李秀彬, 辛良杰, 谈明洪. 耕地资产社会保障功能的空间分异研究——不同农业类型区的比较[J]. 地理科学进展, 2020, 39(9): 1473-1484.
[3]	黄莹泽, 邱炳文, 何玉花, 张珂, 邹凤丽. 东北地区水稻扩张的海拔优势区间分析[J]. 地理科学进展, 2020, 39(9): 1557-1564.
[4]	葛玉娟, 赵宇鸾, 李秀彬. 山区耕地细碎化对土地利用集约度影响——以贵州省亚鱼村为例[J]. 地理科学进展, 2020, 39(7): 1095-1105.
[5]	胡栩, 聂勇, 徐霞, 蒋盛, 张镱锂. 塔里木盆地南缘和田地区土地利用变化的遥感研究[J]. 地理科学进展, 2020, 39(4): 577-590.
[6]	付慧, 刘艳军, 孙宏日, 周国磊. 京津冀地区耕地利用转型时空分异及驱动机制[J]. 地理科学进展, 2020, 39(12): 1985-1998.
[7]	李宇, 邱炳文, 何玉花, 陈功, 叶智燕. 基于MODIS数据的2001—2018年中国耕地复种指数反演研究[J]. 地理科学进展, 2020, 39(11): 1874-1883.
[8]	闫梦露,王柏源,赵小风,钟太洋. 新型职业农民对城市郊区耕地利用“大棚化”转型的影响——以南京市为例[J]. 地理科学进展, 2019, 38(9): 1294-1304.
[9]	李俊, 叶瑜, 魏学琼. 过去300 a大清河上游南部流域耕地变化重建[J]. 地理科学进展, 2019, 38(6): 883-895.
[10]	赵彩杉, 曾刚, 张丽娟, 张学珍. 基于Google Earth和MODIS陆地数据的农林地转换对地表温度的影响——以长江中下游及毗邻地区为例[J]. 地理科学进展, 2019, 38(5): 698-708.
[11]	刘爱琳, 匡文慧, 张弛. 1990-2015年中国工矿用地扩张及其对粮食安全的潜在影响[J]. 地理科学进展, 2017, 36(5): 618-625.
[12]	文高辉, 杨钢桥, 汪文雄, 赵微. 基于农户视角的耕地细碎化程度评价——以湖北省“江夏区—咸安区—通山县”为例[J]. 地理科学进展, 2016, 35(9): 1129-1143.
[13]	范德芹, 赵学胜, 朱文泉, 郑周涛. 植物物候遥感监测精度影响因素研究综述[J]. 地理科学进展, 2016, 35(3): 304-319.
[14]	尹芹, 孟斌, 张丽英. 基于客流特征的北京地铁站点类型识别[J]. 地理科学进展, 2016, 35(1): 126-134.
[15]	罗静, 陈琼, 刘峰贵, 张镱锂, 周强. 青藏高原河谷地区历史时期耕地格局重建方法探讨——以河湟谷地为例[J]. 地理科学进展, 2015, 34(2): 207-.