期刊文献+

长时间序列Radarsat图像的神经网络模拟及土地覆盖变化的快速检测 被引量:2

Radarsat Time Series Analysis and Short-time Change Detection of Regional Land-use/Land-cover
下载PDF
导出
摘要 土地覆盖的短期时空变化模式研究,对土地覆盖的快速、动态监测具有重要意义,也是遥感研究的新热点。本文利用2000—2001年的时间序列Radarsat图像,采用功率谱分析方法,对土地覆盖的短期时—空变化的周期特征进行了分析,由此建立了基于时间序列影像分析的神经网络预测模型,从植被主要生长季节的时间序列雷达卫星影像获取训练样本,对研究区域的典型土地覆盖的短期动态变化过程进行了学习。学习后的模型能够利用多个时间序列的Radarsat影像对下一时刻的影像进行模拟,并进一步检测变化。在模拟结果基础上,定义相对变化距离函数和检测门限,对模拟影像及实际影像中的变化区域进行了检测。检测精度范围在66.67%(农村居民点)—91.67%(水体)之间,平均检测精度为81.66%。由于时间序列信号的引入,神经网络模型能够较好地获取土地覆盖的短期动态变化信息。 Regional dynamic monitoring is gaining rising interests in landuse/land cover study. In this article, a short-term land use/land cover change detection method was proposed, which takes periodic change of land cove into account and performs change detection between simulated image and actual image. Eight scenes of Radarsat image of Pearl River Delta was used for experiment. First, periodogram analysis was carried out on the time-series data to get the temporal pattern of the study area. Some land cover like paddy, cultivated land, orchard and forest reveal periodic variation during the research span. Thus various temporal dynamics of these land covers should be taken into account to acquire accurate short-term change detection. Then, a time-based neural networkprediction model (TNN) was built for time-series forecasting. Ten types of land cover with different temporal pattern were classified and four scenes of Radarsat images in vegetation growing seasons (April, June, August, October) in 2000 were used for network training. Land-cover type was classified based on their temporal variation. The first three scenes were used as the input and the last scene was used as the output(to be predicted). The training result showed stable and precise simulation of TNN. In the third step, the first three scenes of Radarsat images in 2001 was taken as the input to the network and the forth scene was simulated. Finally, a distance function was defined and change threshold was set for change detection. The simulated result was used to detect the change between simulated image and actual image. The detection assessment shows that neural network simulation could well represent the short-time non-linear change of land use/land cover. The detection precision ranged from 66.67% (rural residential area) to 91.67% (water). Other land cover type like paddy field(83.33% ) and orchard(71.43% ) also got relatively high precision, corresponding to their notable variation in time-series images. The average detection precision reached 81.66% , which is a satisfying result for our primary experiment on short time change detection. To sum up, this article testified the possibility of short-term change detection under dynamic cally changing environment. So far there is still few method applicable for short-term change detection. TNN network proposed in this article is a meaningful attempt for research in this field.
出处 《遥感学报》 EI CSCD 北大核心 2007年第6期931-940,共10页 NATIONAL REMOTE SENSING BULLETIN
基金 香港科研基金会(RGC)(HKU7301/04H) 国家杰出青年基金(编号:40525002) "985工程"GIS与遥感地学应用科技创新平台项目(编号:105203200400006)
关键词 RADARSAT 土地覆盖变化 神经网络预测 变化检测 时间序列 Radarsat land use/land cover change neural network change detection time series analysis
  • 相关文献

参考文献23

  • 1Singh A D. Ingital Change Detection Techniques Using Remotely Sensed Data[ J]. International Journal of Remote Sensing, 1989, 10(6) : 989--1003.
  • 2Elke H, Rainer W, Annette O. Analysing Land-cover Changes in Relation to Environmental Variables in Hesse, Germany [ J ]. Landscape Ecology, 2004, 19: 473--489.
  • 3Andres L, Salas W, Skole D. Fourier Analysis of Multitemporal AVHRR Data Applied to a Land Cover Dlassification [ J ]. International Journal of Remote Sensing, 1994, 15: 1115-- 1121.
  • 4Coppin P, Jonckheere I, Nackaerts K, et al. Digital Change Detection Methods in Ecosystem Monitoring: A Review [ J]. International Journal of Remote Sensing, 2004, 25( 9 ) : 1565-- 1596.
  • 5Lu D, Mausel P, Brondizio, et al. Change Detection Techniques [J]. International Journal of Remote Sensing, 2004, 25(12) : 2365--2407.
  • 6Ronald E, Michele F. Long Sequence Time Series Evaluation Using Standardized Principal Components [ J ]. Photogrammetric Engineering & Remote Sensing, 1993, 59: 1307--1312.
  • 7Lotsch A, Friedl MA, Pinzon J. Spatio-temporal Deconvolution of NDVI Image Sequences Using Independent Component Analysis [ J ]. leee Transactions on Geosclence and Remote Sensing, 2003, 41( 12): 2938--2942.
  • 8Yue T X, Chen S P, Xu B, et al. A Curve-theorem Based Approach for Change Detection and its Application to Yellow River Delta[ J]. International Journal of Remote Sensing , 2002, 23( 11 ) : 2283--2292.
  • 9Lambin E, Strahler A. Change Vector Analysis in Multitemporal Space: A Tool to Detect and Categorize and Cover Change Processing Using High Temporal Resolution Satellite Data [ J]. Remote Sensing of Enviromment, 1994, 48: 231-- 244.
  • 10Lambin E, Strahler A. Indicators of Land Cover Change for Change Vector Analysis in Multitemporal Space at Coarse Spatial Scales[ J]. International Journal of Remote Sensing, 1994, 15 : 2099--2119.

二级参考文献53

  • 1温芳茹.水稻、小麦后向散射特性的实验研究[J].电波科学学报,1994,9(1):36-47. 被引量:3
  • 2汤明.裸地散射特性分析[J].电波科学学报,1994,9(4):69-75. 被引量:7
  • 3张兰生,史培军.建立人地系统动力学 加强环境与生态问题的综合研究[J].中国科学基金,1994,8(3):158-160. 被引量:4
  • 4秦大河主编.中国西部环境演变评估(综合卷)[M].北京:科学出版社,2002..
  • 5中国农业年鉴编委会.中国农业年鉴1999[M].北京:农业出版社,1999..
  • 6Jayantha E Hierachical Maximum-Likelihood Classification for Improved Accuracies[J] . IEEE Transactions on Geoscience and Remote Sensing, 1997,35(4) : 1122-1143.
  • 7Iio Y, Omatu S. Category Classification Method Using A Self- organizing Neural Network [ J ] . INT J Remote Sensing, 1997, 18 ( 4 ) :829-845.
  • 8Justin D, et al. The Effect of Neural-Network Structure on a Multispectral Land-Use/Land-Cover Classification [ J ] . Photogrammetric Engineering & Remote Sensing, 1997, 63(5) : 535-544.
  • 9Ji C Y. Crop Classification Method Using a Self-organizing Neural Network[ R]. Interim Report on Crop Classification Using Neural Networks, 1999.
  • 10Cortijo F J, Perez De La blanca N. A Comparative Study of Some Nonparametric Spectral Classifiers [ J ] . Applications to problems with high overlapping training sets Int J Remote Sensing, 1997,18(6) :1259-1275.

共引文献571

同被引文献29

引证文献2

二级引证文献43

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部