知识引导的稀疏时间序列遥感数据拟合

范菁; 余维泽; 吴炜; 沈瑛

doi:10.11834/jrs.20176434

浏览量 : 0 下载量: 1117 CSCD: 2

PDF
导出
分享
收藏
专辑

知识引导的稀疏时间序列遥感数据拟合
Knowledge-guided fitting method for sparse time series remote sensing data
2017年21卷第5期页码：749-756
收稿：2016-11-01，

录用：2017-2-25，

纸质出版：2017-09
DOI： 10.11834/jrs.20176434
稿件说明：

移动端阅览

范菁, 余维泽, 吴炜, 沈瑛. 2017. 知识引导的稀疏时间序列遥感数据拟合. 遥感学报, 21(5): 749–756 DOI： 10.11834/jrs.20176434.

Fan J, Yu W Z, Wu W and Shen Y. 2017. Knowledge-guided fitting method for sparse time series remote sensing data. Journal of Remote Sensing, 21(5): 749–756 DOI： 10.11834/jrs.20176434.

摘要

在多云多雨的地区，光学遥感存在着获取无云数据困难的难题，这会导致时间序列应用中可用数据匮乏。因此，本文面向稀疏时间序列遥感数据，根据噪声造成遥感影像上归一化差分植被指数(NDVI)被低估的事实，提出了一种知识引导的拟合方法。首先，在遥感影像预处理的基础上，利用先验知识和时序差分法对噪声进行识别和剔除；然后，采用高斯二阶模型对原始数据进行拟合；最后，根据拟合残差更新权重，进行迭代拟合，重复上述过程直至获得稳定的结果。本文以Landsat 8 OLI作为数据源，对浙江省杭州地区的森林数据进行拟合，结果表明：在稀疏时间序列数据的情况下，本文方法与MODIS数据拟合结果的相关系数达到0.92，关键时点(如NDVI峰值点等)的时间误差在5 d；相比当前主流方法的0.88与 8 d具有更高的精度。

Abstract

Time series remote sensing plays an important role in forest monitoring

and the performance of the fitting method of time series Normalized Difference Vegetation Index (NDVI) determines the precision of phenology parameter estimation. However

in cloudy and rainy areas

obtaining cloud free remote sensing data for time series application is greatly difficult

leading to the difficulty in precisely fitting the time series NDVI. We present a knowledge-guided fitting method for sparse time series remote sensing data

given that the NDVI is underestimated by noise. First

we use prior knowledge and time difference method to identify and remove the noise

and then the Gaussian second-order model is used to fit the original data. Third

the iterative fitting is carried out by updating the weights of original data according to the fitting residuals. Finally

this process is repeated until a stable result is obtained. We use NDVI derived from Landsat 8 OLI as the data source to fit the forest data in Hangzhou

Zhejiang province. MODIS data are used as the referenced data to test accuracy of fitting result. The correlation coefficient between the fitting result of sparse time series data and the MODIS data fitting results is 0.92

and the difference of the day of max NDVI is five days

which indicates the proposed can be used to estimate the growth intensity

growth period

and other biological parameters effectively

the result is closer to the MODIS data fitting result compared with the state of art method. Our research provides the knowledge-guided fitting method

which is more effective than the other conventional methods

especially for the sparse time series data. This method reduces the human subjective influence and the excessive disturbance caused by the noise in the original data

making the fitting result more close to the actual fitting curve. The advantages of this method include less input parameters

fast converge

and the stability of fitting result. However

the proposed method suppresses the noise in the fitting processing to obtain relatively smooth fitting curve

which also may lead to the loss of the curve information; further improvements can be performed in the future.

关键词

Keywords

references

Atkinson P M, Jeganathan C, Dash J and Atzberger C. 2012. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, 123: 400–417

Beck P S A, Atzberger C, Høgda K A, Johansen B and Skidmore A K. 2006. Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote sensing of Environment, 100(3): 321–334

Carrão H, Gonçalves P and Caetano M. 2010. A nonlinear harmonic model for fitting satellite image time series: analysis and prediction of land cover dynamics. IEEE Transactions on Geoscience and Remote Sensing, 48(4): 1919–1930

Chen J, Jönsson P, Tamura M, Gu Z H, Matsushita B and Eklundh L. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 91(3/4): 332–344

Fu P and Weng Q H. 2016. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175: 205–214

Hird J N and McDermid G J. 2009. Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sensing of Environment, 113(1): 248–258

Jönsson P and Eklundh L. 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1824–1832

Jönsson P and Eklundh L. 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers and Geosciences, 30(8): 833–845

Kim S R, Prasad A K, El-Askary H, Lee W K, Kwak D A, Lee S H and Kafatos M. 2014. Application of the Savitzky-Golay filter to land cover classification using temporal MODIS vegetation indices. Photogrammetric Engineering and Remote Sensing, 80(7): 675–685

Ma T, Zhou C H, Pei T, Haynie S and Fan J F. 2012. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: a comparative case study from China’s cities. Remote Sensing of Environment, 124: 99–107

Mello M P, Vieira C A O, Rudorff B F T, Aplin P, Santos R D C and Aguiar D A. 2013. STARS: a new method for multitemporal remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 1897–1913

Roerink G J, Menenti M and Verhoef W. 2010. Reconstructing cloud free NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9): 1911–1917

Verbesselt J, Hyndman R, Newnham G and Culvenor D. 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1): 106–115

Zhu Z and Woodcock C E. 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: an algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment, 152: 217–234

杨浩, 黄文江, 王纪华, 杨贵军, 屠乃美, 杨小冬, 王大成. 2011. 基于HJ-1A/1B CCD时间序列影像的水稻生育期监测. 农业工程学报, 27(4): 219–224

Yang H, Huang W J, Wang J H, Yang G J, Tu N M, Yang X D and Wang D C. 2011. Monitoring rice growth stages based on time series HJ-1A/1B CCD images. Transactions of the Chinese Society of Agricultural Engineering, 27(4): 219–224

张霞, 李儒, 岳跃民, 刘波, 刘海霞. 2010. 谐波改进的植被指数时间序列重建算法. 遥感学报, 14(3): 437–447

Zhang X, Li R, Yue Y M, Liu B and Liu H X. 2010. Improved algorithm for reconstructing vegetation index image time series based on Fourier harmonic analysis. Journal of Remote Sensing, 14(3): 437–447

文章被引用时，请邮件提醒。

提交

基于高斯混合模型的风云四号快速扫描成像仪晴空图像合成

典型陆表稳定目标热红外辐射特性分析及适用性评估

结合光吸收的近海水体硅藻浓度反演模型构建

Landsat 8全色影像阴影的扁平冰山出水高度提取

Landsat 8 地表温度产品降尺度深度学习方法研究