采用不变点群法(temporally invariant cluster, TIC),由两时相影像的NDVI点密度图确定两个TIC中心并建立辐射归化处理方程,对影像的NDVI进行相对辐射归化处理。与其它辐射归化处理方法相比,该方法简单、有效且精度高,对轻微的植被物候变化敏感且影像不变特征点较少时也能进行辐射归化处理,可有效应用于土地利用/覆盖、植被物候及景观等变化监测。
Radiometric consistency is hard to maintain between separate images due to variations in atmospheric conditions, solar illumination angles, sensor characteristics and sensor view angle. Therefore, radiometric corrections are often performed on multi-temporal imagery to any or all of the variations. This article reports a new relative radiometric normalization technique of multi-temporal satellite imagery of the same terrain. In this study, image data in Xiamen area were acquired on March 4, 2001 and March 26, 2003 (Landsat 7), and the newly developed temporally invariant cluster (TIC) method was used to normalize the normalized difference vegetation index (NDVI) of multi-temporal imagery directly. The TIC centers were identified via a point density map of NDVI pixels from the base image and the target image, and a normalization regression line was created to intersect the TIC centers in point density map of NDVI. Target image NDVI values were then recalculated to base image radiometric level using the regression function so that these two images could be compared on a common radiometric scale. After normalization, the accuracy of NDVI was significantly improved. The TIC method provides a simple, effective and repeatable method for radiometric normalization. Compared to previous relative radiometric normalization methods, the new method does not require high level programming and statistical skills. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. This normalization method is effective for detection of a range of land use, land cover, and phenological changes.
[1] Du Y, Teillet P M, CihlarJ. Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection[J]. Remote Sensing of Environment, 2002, 82: 123-134.
[2] 李晓兵, 陈云浩, 王宏,等. 中国土地覆盖动态变化幅度的区域分异规律[J].地理科学, 2004, 24 (3): 270~274.
[3] 张友水, 冯学智, 周成虎. 多时相TM影像相对辐射校正研究[J].测绘学报, 2006, 35 (2): 122~127.
[4] 刘小平, 邓孺孺, 彭晓鹃. 基于TM影象的快速大气校正方法[J].地理科学, 2005, 25 (1): 87~93.
[5] 吕恒, 江南, 罗潋葱. 基于TM数据的太湖叶绿素A浓度定量反演[J].地理科学, 2006, 26 (4): 472~476.
[6] Hall F G, Strebel D E, Nickeson J E,et al. Radiometric rectification: Toward a common radiometric response among multidate, multisensor images [J]. Remote Sensing of Environment, 1991, 35: 11-27.
[7] Myneni R B, Keeling C D, Tucker C J, et al. Increased plant growth in the northern high latitudes from 1981 to 1991 [J]. Nature, 1997, 386: 698-702.
[8] 刘三超, 张万昌, 蒋建军,等.用TM影像和DEM获取黑河流域地表反射率和反照率[J].地理科学, 2003, 23 (5): 585~591.
[9] 刘传胜,张万昌,雍斌.基于两种新型景观指数的张掖绿洲植被格局动态研究[J].地理科学,2008,28(1):59~65.
[10] 张友水,谢元礼.MODIS影像的NDVI和LSWI植被水分含量估算[J].地理科学,2008,28(1):72~76.
[11] 王芳,卓莉,黎夏,等.基于高光谱特征选择和RBFNN的城市植被胁迫程度监测[J].地理科学,2008,28(1): 77~82.
[12] 邓玉娇,匡耀求,黄宁生,等.温室效应增强背景下城市热环境变化的遥感分析——以广东省东莞市为例[J].地理科学,2008,28(6):819~824.
[13] Chavez P S. Image-based atmospheric corrections—Revisited and improved [J]. Photogrammetric Engineering and Remote Sensing, 1996, 62: 1025-1036.
[14] Schott J R, Selvaggio C, Volchok W J. Radiometric Scene Normalization Using Pseudo—invariant Features [J]. Remote Sensing of Environment, 1988, 26: 1-16.
[15] Song C, Woodcock C E, Seto K C, et al. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects [J]. Remote Sensing of Environment, 2001, 75: 230-244.
[16] Cohen W B, Maiersperger T K., Gower S T, et al. An improved strategy for regression of biophysical variables and Landsat ETM+data [J]. Remote Sensing of Environment, 2003a, 84: 561-571.
[17] Cohen W B, Maiersperger T K, Yang Z, et al. Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: A quality assessment of 2000/2001 provisional MODIS products [J]. Remote Sensing of Environment, 2003b, 88: 233-255.
[18] 张定祥, 刘顺喜, 尤淑撑,等. 基于机载成像光谱数据的宜兴市土地利用/土地覆盖分类方法对比研究[J]. 地理科学, 2004, 24 (2): 193~198.
[19] Elvidge C D, Yuan D, Weerackoon R D, et al. Relative radiometric normalization of Landsat Multispectral Scanner (MSS) data using an automatic scattergram-controlled regression. Photogrammetric[J].Engineering and Remote Sensing, 1995, 61: 1255-1260.
[20] Xuexia Chen, Lee Vierling, Don Deering. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time [J]. Remote Sensing of Environment, 2005, 98: 63-79.
[21] Lu D, Mausel P, Brondizio E, et al. Change detection techniques [J]. International Journal of Remote Sensing, 2004, 25: 2365-2407.
