Vegetation Cover Change Detection in the Cropping Area Based on TM Image
WANG Xiao-dong1,2, HE Hao1,2, HOU Dong1,2, SUN Guan-nan1,2, ZHU Wen-quan1,2
1. College of Resource Sciences & Technology, Beijing Normal University, Beijing 100875, China; 2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
摘要以交叉相关光谱匹配(cross correlogram spectral matching,CCSM)为基础构建土地覆盖变化强度指标,利用华北农业植被覆盖区2期不同时相的TM图像计算该地区土地覆盖变化强度图像。认为变化强度图像任意二阶邻域中像素的变化强度服从隐马尔可夫模型,用马尔可夫随 机场-最大后验估计(maxium a posteriori estimation of markov random field,MRF-MAP)的方法从变化强度图像中提取植被变化区域。实验证明: 该方法能够有效识别各种外源噪声造成的农业植被覆盖区域同物异谱的现象,可准确提取植被变化区域; 但对于水体区域存在误判现象。
Abstract:In this paper, a change intensity indicator of land cover based on cross correlogram spectral matching (CCSM) technique was employed to generate the change intensity image of the cropping vegetation cover area in North China between two TM images in different periods. It was first considered that the change intensity of image pixel of the two-order neighbor in the change intensity image obeyed the hidden markov random field model, and then the vegetation cover change area was extracted from the change intensity image using maximum a posteriori estimation of markov random field (MRF-MAP) model. The experiment has proved that the proposed method could precisely extract vegetation cover change and inhibit effectively the same object with different spectra due to exogenous noises in the cropping vegetation cover area. However, this method seems to perform unsatisfactorily over the water area.
王晓东, 何浩, 侯东, 孙冠楠, 朱文泉. 基于TM图像的农业区域植被覆盖变化检测 [J]. 国土资源遥感, 2012, 24(2): 92-97.
WANG Xiao-dong, HE Hao, HOU Dong, SUN Guan-nan, ZHU Wen-quan. Vegetation Cover Change Detection in the Cropping Area Based on TM Image. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 92-97.
[1] Lu D,Mausel P,Brondizio E,et al.Change Detection Techniques[J]. International Journal of Remote Sensing,2004,25(12):2365- 2401. [2] 李淑坤,李培军,程涛.加入多时相纹理的遥感变化检测[J].国土资源遥感,2009(3):35-40. [3] 谢仁伟,牛铮,孙睿,等.基于多波段统计检验的土地利用变化检测[J].国土资源遥感,2009(2):66-70. [4] Foody G M.Monitoring the Magnitude of Land-cover Change Around the Southern Limits of the Sahara[J].Photogrammetric Engineering and Remote Sensing,2001,67(7):841-847. [5] 李月臣,陈晋,宫鹏,等.基于NDVI时间序列数据的土地覆盖变化检测指标设计[J].应用基础与工程科学学报,2005,13(3):261-275. [6] 陈晋,何春阳,史培军,等.基于变化向量分析的土地利用/覆盖变化动态监测(I)——变化阈值的确定方法[J].遥感学报,2001,5(4):259-266. [7] 许卫东,尹球,匡定波.地物光谱匹配模型比较研究[J].红外与毫米波学报,2005,24(4):296-300. [8] 申邵洪,万幼川,龚浩,等.遥感影像变化检测自适应阈值分割的Kriging方法[J].武汉大学学报:信息科学版,2009,34(8):902-905. [9] Van der Meer.Spectral Curve Shape Matching with a Continuum Removed CCSM Algorithm[J].International Journal of Remote Sensing,2000,21(16):3179-3185. [10] Chen X H,Jin C,Shen M G,et al.Land-use/Land-cover Change Detection Using Change-vector Analysis in Posterior Probability Space[C]//Proc.SPIE 7144,714405,2008:10.1117/12.812671. [11] 刘永学,李满春,毛亮.基于边缘的多光谱遥感图像分割方法[J].遥感学报,2006,10(3):350-356. [12] Bilmes J A.A Gentle Tutorial of the EM Algorithm and Its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models[R].Technical Report:ICSI.TR-97-02,International Computer Science Institute,Berkeley CA,USA,1998. [13] 刘伟强,陈鸿,夏德深.基于马尔可夫随机场的遥感图像分割和描述[J].东南大学学报:自然科学版,1999,29(11):11-15. [14] 钟家强,王润生.基于自适应参数估计的多时相遥感图像变化检测[J].测绘学报,2005,34(4):331-336. [15] 郑玮,康戈文,陈武凡,等.基于模糊马尔可夫随机场的无监督遥感图像分割算法[J].遥感学报,2008,12(2):246-252. [16] Wang L,Chen J,Gong P,et al.Land Cover Change Detection with a Cross-correlogram Spectral Matching Algorithm[J].International Journal of Remote Sensing,2009,30(12):3259-3273. [17] Zhang Y,Brady M,Smith S.Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-maximization Algorithm[J].IEEE Transactions on Medical Image,2001,20(1):45-57. [18] Liu D S,Kelly M,Gong P.A Spatial-temporal Approach to Monitoring Forest Disease Spread Using Multi-temporal High Spatial Resolution Imagery[J].Remote Sensing of Environment,2006,101(2):167-180. [19] 冯衍秋,梁斌,陈明,等.基于gibbs随机场的有限混合模型改进与脑部MR图像的稳健分割[J].中国生物医学工程学报,2003,22(3):193-198. [20] Besag J.On the Statistical Analysis of Dirty Pictures[J].Journal of the Royal Statistical Society:Series B (Methodological),1986,48(3):259-302. [21] Matthew M W,Adler Gdden S M,Berk A,et al.Atmospheric Correction of Spectral Imagery:Evaluation of the FLAASH Algorithm with AVIRIS Data[C]//Presented at SPIE Proceeding,Algorithm and Technologies for Multispectral,Hyperspectral,and Ultraspectral Imagery IX,2003.