| 光谱学与光谱分析 |
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| Construction of Vegetation Shadow Index (SVI) and Application Effects in Four Remote Sensing Images |
| XU Zhang-hua1, 2, 3, LIU Jian1, 2, 4*, YU Kun-yong1, 2, LIU Tao5, GONG Cong-hong1, 2, 3, TANG Meng-ya1, 2, 3, XIE Wan-jun1, 2, 3, LI Zeng-lu1, 4 |
1. Institute of Geomatics Application, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2. College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
3. Graduate School, Fujian Agriculture and Forestry University, Fuzhou 350002, China
4. Sanming University, Sanming 365000, China
5. College of Nature Conservation, Beijing Forestry University, Beijing 100083, China |
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Abstract Taking the images of Landsat TM, ALOS AVNIR-2, CBERS-02B CCD and HJ-1 CCD as the experimental data, for increasing the differences among shaded area, bright area and water further, the present paper construed a novel vegetation index-Shaded Vegetation Index(SVI), which can not only keep the absolute differences among bright area, shaded area and water area in the near-infrared band, but also can enlarge NDVI, eliminate the possible mixes, and change the histogram “skewed” phenomenon of NDVI, so the vegetation index value is closer to normal distribution, and more in line with the filed condition; this new index was applied to the surface features of large difference of the near-infrared radiation characteristics. Verified by accuracy assessment for the bright area, shaded area and water area recognition effects with SVI, it was showed that the overall classification accuracies of these images were up to 98.89%, 100%, 97.78% and 97.78% respectively, with the overall Kappa statistics of 0.983 3, 1, 0.966 7, and 0.966 7, indicating that SVI has excellent detection effects for bright area, shaded area and water area; the statistical comparison of sub-images between SVI and NDVI also illustrated the reliability and effectiveness of SVI, which can be applied in the shadow removal for remote sensing images.
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Received: 2013-03-11
Accepted: 2013-06-21
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Corresponding Authors:
LIU Jian
E-mail: fjliujian@126.com
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[1] Le H M S, Andre C. ISPRS Journal of Photogrammetry and Remote Sensing, 2008, 64(4): 351.
[2] Li Y, Gong P, Sasagawa T. International Journal of Remote Sensing, 2005, 26(18): 3911.
[3] YAO Chen, HUANG Wei, LI Yuan-hua(姚 晨, 黄 微, 李元华). Remote Sensing Technology and Application(遥感技术与应用), 2009, 24(4): 496.
[4] Rudin L, Osher S, Fatemi E. Physica D, 1992, 60(1): 259.
[5] WANG Shu-gen, WANG Jun-li, WANG Ai-ping(王树根, 王军利, 王爱萍). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2006, 31(8): 663.
[6] PU Zhi, YANG Liao, BAI Jie(蒲 智, 杨 辽, 白 洁). Remote Sensing Technology and Application(遥感技术与应用), 2008, 23(6): 735.
[7] YU Dong-fang, YIN Jian-ping, ZHANG Guo-min(于东方, 殷建平, 张国敏). Computer Engineering & Science(计算机工程与科学), 2008, 30(12): 43，44, 93.
[8] Rouse J W, Haas R H, Schell J A, et al. Proceedings of Third Earth Resources Technology Satellite 21 Symposium Greenbelt, 1974. 48.
[9] DING Li-dong, YU Wen-hua, QIN Zhi-hao, et al(丁莉东, 余文华, 覃志豪, 等). Remote Sensing for Land & Resources(国土资源遥感), 2007, (1): 82.
[10] Lee Z P, Carder K, Amone R. Applied Optics, 2002, 41(27): 5755.
[11] GUO Peng, ZOU Chun-hui, ZHAO Xue-bin(郭 鹏, 邹春辉, 赵学斌). 2011 10th Chinese Meteorological Society Annual Meeting(2011年第二十八届中国气象学会年会)，2011.
[12] LIU Chen-zhou, SHI Jian-cheng, GAO Shuai, et al(刘晨洲, 施建成, 高 帅, 等). Remote Sensing Information(遥感信息), 2010, (1): 84. |
| [1] |
XU Zhang-hua1, 2, LI Cong-hui5, LIU Jian2, 3, 4*, YU Kun-yong2, 3, GONG Cong-hong2, 3, TANG Meng-ya2, 3, LAI Ri-wen2, 3. Construction of Age Group Vegetation Index and Preliminary Application [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(06): 1629-1634. |
| [2] |
ZHANG Ting1,2, DING Jian-li1,2,3*，WANG Fei1,2. Simulation of Image Multi-Spectrum Using Field Measured Endmember Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(11): 2889-2893. |
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