| 光谱学与光谱分析 |
|
|
|
|
|
| Sophisticated Vegetation Classification Based on Feature Band Set Using Hyperspectral Image |
| SHANG Kun1,2, ZHANG Xia1*, SUN Yan-li1,3, ZHANG Li-fu1, WANG Shu-dong1, ZHUANG Zhi1 |
1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China
3. University of Chinese Academy of Sciences, Beijing 100049, China |
|
|
|
|
Abstract There are two major problems of sophisticated vegetation classification (SVC) using hyperspectral image. Classification results using only spectral information can hardly meet the application requirements with the needed vegetation type becoming more sophisticated. And applications of classification image are also limited due to salt and pepper noise. Therefore the SVC strategy based on construction and optimization of vegetation feature band set (FBS) is proposed. Besides spectral and texture features of original image, 30 spectral indices which are sensitive to biological parameters of vegetation are added into FBS in order to improve the separability between different kinds of vegetation. And to achieve the same goal a spectral-dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed. A spatial-dimension optimization algorithm of FBS based on neighborhood pixels’ spectral angle distance (NPSAD) is proposed so that detailed information can be kept during the image smoothing process. The results of SVC experiments based on airborne hyperspectral image show that the proposed method can significantly improve the accuracy of SVC so that some widespread application prospects like identification of crop species, monitoring of invasive species and precision agriculture are expectable.
|
|
Received: 2014-05-24
Accepted: 2014-08-29
|
|
|
|
Corresponding Authors:
ZHANG Xia
E-mail: zhangxia@radi.ac.cn
|
|
[1] Tong Q X, Xue Y Q, Zhang L F. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 70.
[2] Bioucas-Dias J, Plaza A, Camps-Valls G, et al. Geoscience and Remote Sensing Magazine, IEEE, 2013, 1(2): 6.
[3] Plaza A, Benediktsson J A, Boardman J W, et al. Remote Sensing of Environment, 2009, 113: S110.
[4] Jia K, Wu B F, Tian Y C, et al. International Journal of Remote Sensing, 2011, 32(24): 9307.
[5] Tong Q X, Xue Y Q, Wang J N, et al. Journal of Remote Sensing, 2010，(3): 409.
[6] Haralick R M, Shanmuga.K, Dinstein I. IEEE Transactions on Systems Man and Cybernetics, 1973, Smc3(6): 610.
[7] Mallinis G, Koutsias N, Tsakiri-Strati M, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2008, 63(2): 237.
[8] Anselin L. Geographical Analysis, 1995, 27(2): 93.
[9] Getis A, Ord J K. Geographical Analysis, 1992, 24(3): 189.
[10] Rouse Jr J, Haas R, Schell J, et al. NASA Special Publication, 1974, 351: 309.
[11] Blackburn G A. International Journal of Remote Sensing, 1998, 19(4): 657.
[12] Gitelson A, Merzlyak M N. Journal of Plant Physiology, 1994, 143(3): 286.
[13] Penuelas J, Gamon J A, Griffin K L, et al. Remote Sensing of Environment, 1993, 46(2): 110.
[14] Barnes J D, Balaguer L, Manrique E, et al. Environmental and Experimental Botany, 1992, 32(2): 85.
[15] Gamon J A, Penuelas J, Field C B. Remote Sensing of Environment, 1992, 41(1): 35.
[16] Hernandez-Clemente R, Navarro-Cerrillo R M, Suarez L, et al. Remote Sensing of Environment, 2011, 115(9): 2360.
[17] Schleicher T, Bausch W, Delgado J, et al. 2001 ASAE Annual International Meeting, St-Joseph, MI, USA. ASAE Paper, 2001(01-11151).
[18] Penuelas J, Filella I. Trends in Plant Science, 1998, 3(4): 151.
[19] Broge N H, Leblanc E. Remote Sensing of Environment, 2001, 76(2): 156.
[20] Daughtry C S T, Walthall C L, Kim M S, et al. Remote Sensing of Environment, 2000, 74(2): 229.
[21] Haboudane D, Miller J R, Pattey E, et al. Remote Sensing of Environment, 2004, 90(3): 337.
[22] Rondeaux G, Steven M, Baret F. Remote Sensing of Environment, 1996, 55(2): 95.
[23] Meroni M, Rossini M, Guanter L, et al. Remote Sensing of Environment, 2009, 113(10): 2037.
[24] Roujean J L, Breon F M. Remote Sensing of Environment, 1995, 51(3): 375.
[25] Zarco-Tejada P J, Berjon A, Lopez-Lozano R, et al. Remote Sensing of Environment, 2005, 99(3): 271.
[26] Gitelson A A, Merzlyak M N, Chivkunova O B. Photochemistry and Photobiology, 2001, 74(1): 38.
[27] Gitelson A A, Keydan G P, Merzlyak M N. Geophysical Research Letters, 2006, 33(11).
[28] Zarco-Tejada P J, Gonzalez-Dugo V, Berni J A J. Remote Sensing of Environment, 2012, 117: 322.
[29] Mahlein A K, Rumpf T, Welke P, et al. Remote Sensing of Environment, 2013, 128: 21.
[30] Chavez P S, Berlin G L, Sowers L B. Journal of Applied Photographic Engineering, 1982, 8(1): 23.
[31] Kailath T. Communication Technology, IEEE Transactions on, 1967, 15(1): 52.
[32] Schmidt K S, Skidmore A K. Remote Sensing of Environment, 2003, 85(1): 92.
[33] Geng X R, Zhang X, Cheng Z C, et al. Journal of Infrared and Millimeter Waves, 2004, 23(4). |
| [1] |
ZHANG Jie1, 2, XU Bo1, FENG Hai-kuan1, JING Xia2, WANG Jiao-jiao1, MING Shi-kang1, FU You-qiang3, SONG Xiao-yu1*. Monitoring Nitrogen Nutrition and Grain Protein Content of Rice Based on Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1956-1964. |
| [2] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
| [3] |
YANG En1, WANG Shi-bo2*. Study on Directional Near-Infrared Reflectance Spectra of Typical Types of Coal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 847-858. |
| [4] |
JIANG Jie1, YU Quan-zhou1, 2, 3*, LIANG Tian-quan1, 2, TANG Qing-xin1, 2, 3, ZHANG Ying-hao1, 3, ZHANG Huai-zhen1, 2, 3. Analysis of Spectral Characteristics of Different Wetland Landscapes Based on EO-1 Hyperion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 524-529. |
| [5] |
XIA Tian1*, YANG Ke-ming2, FENG Fei-sheng3, GUO Hui4, ZHANG Chao2. A New Copper Stress Vegetation Index NCSVI Explores the Sensitive Range of Corn Leaves Spectral Under Copper Pollution[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2604-2610. |
| [6] |
WANG Xin-hui1, 2, GONG Cai-lan1, 2*, HU Yong1, 2, LI Lan1, 2, HE Zhi-jie1, 2. Spectral Feature Construction and Sensitivity Analysis of Water Quality Parameters Remote Sensing Inversion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1880-1885. |
| [7] |
KANG Xiao-yan1,2, ZHANG Ai-wu1,2*, PANG Hai-yang1,2. Estimation of Grassland Aboveground Biomass From UAV-Mounted Hyperspectral Image by Optimized Spectral Reconstruction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 250-256. |
| [8] |
CHEN Yan-long1,2, WANG Xiao-lan3, LI En3, SONG Mei-ping3, BAO Hai-mo4*. Research and Application of Band Selection Method Based on CEM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3778-3783. |
| [9] |
WANG Qiang-hui1, HUA Wen-shen1*, HUANG Fu-yu1, ZHANG Yan1, YAN Yang2. Hyperspectral Anomaly Detection Based on Approximate Posterior Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(08): 2538-2545. |
| [10] |
LIN Yi1, LIU Si-yuan1, YAN Lei1, FENG Hai-kuan2, ZHAO Shuai-yang1, ZHAO Hong-ying1*. Improvement of Hyperspectral Estimation of Nitrogen Content in Winter Wheat by Leaf Surface Polarized Reflection Measurement[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1956-1964. |
| [11] |
CHEN Yao1, 2, HUANG Chang-ping1*, ZHANG Li-fu1, QIAO Na1, 2. Spectral Characteristics Analysis and Remote Sensing Retrieval of COD Concentration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 824-830. |
| [12] |
CHEN Xiu-qing, YANG Qi, HAN Jing-ye, LIN Lin, SHI Liang-sheng*. Estimation of Winter Wheat Leaf Water Content Based on Leaf and Canopy Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 891-897. |
| [13] |
YUAN Zi-ran, WEI Li-fei*, ZHANG Yang-xi, YU Ming, YAN Xin-ru. Hyperspectral Inversion and Analysis of Heavy Metal Arsenic Content in Farmland Soil Based on Optimizing CARS Combined with PSO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 567-573. |
| [14] |
YE Fa-wang, WANG Jian-gang*, QIU Jun-ting, ZHANG Chuan. A Geological Application Oriented Comparison Research on Different Atmospheric Correction Methods for Airborne CASI-SASI Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2677-2685. |
| [15] |
TAO Chao1, CUI Wen-bo1, WANG Ya-jin1, ZOU Bin1, 2*, ZOU Zheng-rong1. Soil Heavy Metal Qualitative Classification Model Based on Hyperspectral Measurements and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2602-2607. |
|
|
|
|
