| 光谱学与光谱分析 |
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| Comparison of Performances in Retrieving Impervious Surface between Hyperspectral (Hyperion) and Multispectral (TM/ETM+) Images |
| TANG Fei, XU Han-qiu* |
| Institute of Remote Sensing Information Engineering, Fuzhou University, College of Environment and Resources, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection, Fuzhou University, Fuzhou 350116, China |
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Abstract The retrieval of impervious surface is a hot topic in the remote sensing field in the past decade. Nevertheless, studies on retrieving impervious surface from hyperspectral image and the comparison of the performances in retrieving impervious surface between hyperspectral and multispectral images are rarely reported. Therefore, The present paper focuses on the characteristics of hyperspectral (EO-1 Hyperion) and multispectral (Landsat TM/ETM+) images and implements a complementary study on the comparison based on the retrieved impervious surface information between Hyperion and TM/ETM+ data. For up to 242 bands of Hyperion image, a further study was carried out to select feature bands for impervious surface retrieving using stepwise discriminant analysis. As a result, 11 feature bands were selected and a new image named Hyperion’ was thus composed. The new Hyperion’ image was used to investigate whether this band-reduced image could obtain higher accuracy in retrieving impervious surface. The three test regions were selected from Fuzhou, Guangzhou and Hangzhou of China, with date-coincident or nearly coincident image pairs of the used sensors. The linear spectral mixture analysis (LSMA) was employed to retrieve impervious surface and the results were accessed for their accuracy. The comparison shows that the Hyperion image has higher accuracy than TM/ETM+, and the Hyperion’ composed of the selected 11 feature bands has the highest accuracy. The advantages of Hyperion in spectral and radiometric resolutions over TM/ETM+ are believed to be the main factors contributing to the higher accuracy. The high spectral and radiometric resolutions of Hyperion image allow the sensor to have higher sensitivity in distinguishing subtle spectral changes of ground objects. While, the highest accuracy the 11-band Hyperion’ image achieved is owing to the significant reduction of the band dimension of the image and thus the band redundancy.
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Received: 2013-07-01
Accepted: 2013-10-25
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Corresponding Authors:
XU Han-qiu
E-mail: hxu@fzu.edu.cn
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[1] Ridd M K. International Journal of Remote Sensing, 1995, 16(12): 2165.
[2] Wu C, Murray A T. Remote Sensing of Environment, 2003, 84: 493.
[3] Yang F, Matsushita B, Fukushima T. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(7): 479.
[4] Lu D, Moran E, Hetrick S. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(7): 298.
[5] ZHAO Xiao-feng, QIU Quan-yi(赵小锋, 邱全毅). Remote Sensing Technology and Application(遥感技术与应用), 2009, 24(6): 737.
[6] CHENG Xi, SHEN Zhan-feng, LUO Jian-cheng, et al(程 熙, 沈占锋, 骆剑承, 等). Journal of Remote Sensing(遥感学报), 2011, 15(6): 1228.
[7] LI Bo, HUNAG Jing-feng, WU Ci-fang(李 波, 黄敬峰, 吴次芳). Journal of Natural Resources(自然资源学报), 2012, 27(9): 1590.
[8] NASA. Landsat 7 Science Data Users Handbook [2012-08-05]. http://landsathandbook.gsfc.nasa.gov.
[9] XU Han-qiu(徐涵秋). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2007, 32(1): 62.
[10] Chavez P S, jr. Photogrammetric Engineering and Remote Sensing, 1996, 62 (9): 1025.
[11] Weng Q, Hu X, Lu D. International Journal of Remote Sensing, 2008, 29(11): 3209.
[12] XU Han-qiu, ZHANG Tie-jun, LI Chun-hua(徐涵秋, 张铁军, 李春华). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2011, 36(8): 940.
[13] Pu R, Bell S, Meyer C, et al. Estuarine, Coastal and Shelf Science, 2012, 115: 234.
[14] TANG Fei, XU Han-qiu(唐 菲, 徐涵秋). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2013, 38(6): 34.
[15] XU Han-qiu, TANG Fei(徐涵秋, 唐 菲). Acta Ecologica Sinica(生态学报), 2013, 33(11)：3249. |
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