| 光谱学与光谱分析 |
|
|
|
|
|
| Research on the Impact of Absorption Feature Extraction on Spectral Difference Between Similar Minerals |
| ZHAO Heng-qian1, ZHAO Xue-sheng1*, CEN Yi2, YANG Hang2 |
1. College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
2. Hyperspectral Remote Sensing Application Division, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China |
|
|
|
|
Abstract Diagnostic absorption features can indicate the existence of specific materials, which is the foundation of mineral analysis with optical remote sensing data. In hyperspectral data processing, the most commonly used method to extract absorption feature, is Continuum Removal (CR). As for multispectral data, Principle Component Analysis and other indirect methods were used to extract absorption information, and little research has been done on full-band absorption feature extraction. Classification of similar minerals is one of the major difficulties in mineral spectral analysis, while there is no valid index for spectral difference between similar mineral groups. Absorption feature extraction may improve the classification accuracy, but there is no research to investigate the impact of absorption feature extraction on spectral difference between similar minerals. This paper summarized the principle of mineral spectral difference, and proposed the concept of Class Separability Ratio (CSR), which was verified to be a valid index for spectral difference between similar mineral categories. Through comparison experiments on alunite and kaolinite spectra, including USGS spectral library spectra and resampled spectra in accordance with the band settings of HYPERION, ASTER and OLI, the impact of absorption feature extraction on spectral difference between similar minerals were investigated. Experimental results show that valid absorption feature extraction can greatly enhance the spectral difference between similar minerals, and the spectral difference is positively correlated with spectral resolution. Besides, the results of CR can be severely affected by spectral resolution and band center positions, and the absorption feature spectra extraction results for multispectral datasets need to be improved. This research laid the foundation of precise identification between similar mineral categories, and provided valuable reference for the band settings of future geology remote sensing sensors.
|
|
Received: 2016-03-12
Accepted: 2016-08-06
|
|
|
|
Corresponding Authors:
ZHAO Xue-sheng
E-mail: zxs@cumtb.edu.cn
|
|
[1] Asadzadeh S, de Souza Filho C R. Remote Sensing of Environment, 2016, 173: 162.
[2] Weber A K, Russell J M, Goudge T A, et al. Journal of Paleolimnology, 2015, 54(2-3): 253.
[3] Hewson R, Robson D, Mauger A, et al. Journal of Spatial Science, 2015, 60(2): 207.
[4] Rodger A, Laukamp C, Haest M, et al. Remote Sensing of Environment, 2012, 118: 273.
[5] Xu Y, Zhang Z, Hu G. Journal of the Indian Society of Remote Sensing, 2010, 38(2): 169.
[6] Magendran T, Sanjeevi S. International Journal of Applied Earth Observation and Geoinformation, 2014, 26: 413.
[7] Kokaly R F, Despain D G, Clark R N, et al. Remote Sensing of Environment, 2003, 84(3): 437.
[8] Kokaly R F, Couvillion B R, Holloway J M, et al. Remote Sensing of Environment, 2013, 129: 210.
[9] Knight E J, Kvaran G. Remote Sensing, 2014, 6(11): 10286.
[10] Yamaguchi Y, Kahle A B, Tsu H, et al. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(4): 1062.
[11] Liu B, Shen W, Lin N, et al. Journal of Applied Remote Sensing, 2014, 8(1): 83630.
[12] Bishop C A, Liu J G, Mason P J. International Journal of Remote Sensing, 2011, 32(9): 2409.
[13] Rajendran S, Nasir S. International Journal of Remote Sensing, 2013, 34(8): 2763.
[14] Rahimzadegan M, Sadeghi B, Masoumi M, et al. Arabian Journal of Geosciences, 2014: 1.
[15] Debba P, Carranza E J, Van der Meer F D, et al. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(12): 3649.
[16] Li Q, Niu C. Journal of Applied Remote Sensing, 2015, 9(1): 96008.
[17] Rodger A, Cudahy T. Remote Sensing of Environment, 2009, 113(10): 2243.
[18] Kruse F A, Lefkoff A B, Boardman J W, et al. Remote Sensing of Environment, 1993, 44(2): 145.
[19] ZHAO Heng-qian，ZHANG Li-fu，CEN Yi, et al(赵恒谦，张立福，岑 奕, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2013, 32(6): 563. |
| [1] |
LI Xin-xing1, LIANG Bu-wen1, BAI Xue-bing1, LI Na2*. Research Progress of Spectroscopy in the Detection of Soil Moisture Content[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3705-3710. |
| [2] |
CEN Yi1, ZHANG Lin-shan1，2, SUN Xue-jian1*, ZHANG Li-fu1, LIN Hong-lei1, ZHAO Heng-qian3, WANG Xue-rui4. Spectral Analysis of Main Mineral Pigments in Thangka[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1136-1142. |
| [3] |
ZHANG Lei1, ZHAO Jian-long2, JIA Kun3*, LI Xiao-song1 . Plant Spectral Discrimination Based on Phenological Features [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(10): 2836-2840. |
| [4] |
LI Xiang-ru1, FENG Chun-ming2, WANG Yong-jun1, LU Yu1 . A Novel Spectrum Feature Extraction Method [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(10): 2856-2860. |
| [5] |
GAI Ying-ying1,2，FAN Wen-jie1*，XU Xi-ru1，YAN Bin-yan1，WANG Huan-jiong3,4，LIU Yuan1 . Flower Species Identification and Coverage Estimation Based on Hyperspectral Remote Sensing Data in Hulunbeier Grassland [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(10): 2778-2783. |
| [6] |
ZHAO Zhao1，LI Xia1*，YIN Ye-biao2，TANG Jin1，ZHOU Sheng-bin3 . Analysis of Spectral Features Based on Water Content of Desert Vegetation [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(09): 2500-2503. |
| [7] |
LI Xiang-ru1, 2, 5,HU Zhan-yi1,ZHAO Yong-heng3,LI Xiao-ming4 . RVM Supervised Feature Extraction and Seyfert Spectra Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(06): 1702-1706. |
| [8] |
LI Xiang-ru1,HU Zhan-yi1*,ZHAO Yong-heng2. Supervised Feature Extraction Based on FDA and Galaxy Spectra Classification [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(09): 1898-1901. |
|
|
|
|
