|
|
|
|
|
|
| Spectral Reflectance Characteristics of Alpine Grassland Based on Derivative and Logarithmic Transform Spectra —Take HJ-1A/HSI Images of Naqu Prefecture as an Example |
| LIU Wei, SUN Hai-xia, YANG Xiao-bo, DONG Jian-min |
| Xizang Key Laboratory of Optical Information Processing and Visualization Technology, Xizang Minzu University, Xianyang 712082, China |
|
|
|
|
Abstract This paper points out KICA-NFCM algorithm to identify 4 alpine grassland types using HSI hyper-spectral images, by the comparative study of three spectra and two algorithms. Spectral reflectance data for stipa purpurea, kobresia tibetica, little kobresia and kobresia pygmaea was collected from HSI images, based on field investigation and inspection on the spot. Logarithm transformation and derivative transformation were used in the original spectra of 4 alpine grassland types. Sensitivity bands were determined for original spectra data, first-derivative spectra and logarithmic transform spectra, after the application of waveform analysis, one-way ANOV and correlation analysis. Then, sensitivity bands were imported into KICA-NFCM algorithm to identify 4 alpine grassland types mentioned above. For the sake of contrast, ICA-FCM algorithm was tested too. For original spectra data，first-derivative spectra，and logarithmic transform spectra, sensitivity bands were as follows: 788~925, 711~742, 669~682 and 788~925 nm respectively. Based on original spectra data，first-derivative spectra，and logarithmic transform spectra using KICA-NFCM algorithm, overall classification accuracy and KAPPA coefficients were as follows: 75.38%, 0.685; 81.26%, 0.752; 87.65%, 0.823. In contrast, overall classification accuracy and KAPPA coefficients were as follows: 64.39%, 0.569; 67.74%, 0.604; 73.14%, 0.662, based on three types of spectra using ICA-FCM algorithm. Results show that comparing with original spectra data and first-derivative spectra using ICA-FCM algorithm, logarithmic transform spectra using KICA-NFCM algorithm can make a more accurate and efficient identification of 4 alpine grassland types mentioned above, as well as the “salt and pepper noise” was suppressed in classed images. In contrast, ICA-FCM algorithm decreased boundary precision of patch in classed images and region consistency. Using “logarithmic transform spectra / ICA-FCM algorithm” proposed in this paper, the above 4 alpine grassland types in Naqu prefecture can be identified more accuracy. This method provides technical foundations for the development of hyper-spectral imaging observation for alpine grassland.
|
|
Received: 2018-11-26
Accepted: 2019-05-30
|
|
|
|
[1] WEI Xiu-hong, QIN Gui-li, FAN Yan-min, et al(魏秀红, 靳瑰丽, 范燕敏, 等). Chinese Journal of Grassland(中国草地学报), 2017, 39(6): 33.
[2] CHEN Yong-qiang, CHEN Biao, LEI Xin-ming, et al(陈永强, 陈 标, 雷新明, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(11): 3483.
[3] CONG Meng-long, SUN Dan-dan, WANG Yi-ding(丛梦龙，孙丹丹，王一丁). Infrared and Laser Engineering(红外与激光工程)，2017，46(2)：226.
[4] Kaur A, Kumar R, Kaur S. International Journal of Computer Applications, 2017, 158(10): 5.
[5] Liu G Y, Zhang Y, Wang A M. IEEE Transactions on Image Processing, 2015, 24(11): 3990.
[6] AN Ru, LU Cai-hong, WANG Hui-lin, et al(安 如, 陆彩红, 王慧麟, 等). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2018, 43(3): 399.
[7] YANG Ke-ming, XIA Tian, LIU Yi-cong, et al(杨可明，夏 天，刘一聪，等). Journal of China University of Mining & Technology(中国矿业大学学报)，2018，47(3)：691.
[8] LI Chang-chun, CHEN Peng, LU Guo-zheng, et al(李长春, 陈 鹏, 陆国政, 等). Chinese Journal of Applied Ecology(应用生态学报), 2018, 29(4): 1225.
[9] LI Shao-ping, WU Zheng-fang, ZHAO Yun-sheng, et al(李少平，吴正方，赵云升，等). J. Infrared Millim. Waves(红外与毫米波学报)，2016，35(5)：584.
[10] WANG Jian-yu, LI Chun-lai, LÜ Gang, et al(王建宇, 李春来, 吕 刚, 等). J. Infrared Millim. Waves(红外与毫米波学报), 2017, 36(1): 69. |
| [1] |
ZHANG Xiao-yan, HOU Xue-hui, WANG Meng, WANG Li-li*, LIU Feng*. Study on Relationship Between Photosynthetic Rate and Hyperspectral Indexes of Wheat Under Stripe Rust Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 940-946. |
| [2] |
LIU Yang1, 4, 5, ZHANG Han2, FENG Hai-kuan1, 3, 5*, SUN Qian1, 5, HUANG Jue4, WANG Jiao-jiao1, 5, YANG Gui-jun1, 5. Estimation of Potato Above Ground Biomass Based on Hyperspectral Images of UAV[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2657-2664. |
| [3] |
YANG Pei-qi1, 2, 3, LIU Zhi-gang1, 2, 3*，NI Zhuo-ya1, 2, 3, WANG Ran1, 2, 3，WANG Qing-shan1, 2, 3 . Remote Sensing of Chlorophyll Fluorescence at Airborne Level Based on Unmanned Airship Platform and Hyperspectral Sensor [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(11): 3101-3105. |
| [4] |
WANG Ran1, 2, 3, LIU Zhi-gang1, 2, 3*, FENG Hai-kuan4, YANG Pei-qi1, 2, 3, WANG Qing-shan1, 2, 3, NI Zhuo-ya1, 2, 3 . Extraction and Analysis of Solar-Induced Chlorophyll Fluorescence of Wheat with Ground-Based Hyperspectral Imaging System [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(09): 2451-2454. |
| [5] |
WANG Da-cheng1, 2, ZHANG Dong-yan1, 2, ZHAO Jin-ling1, LI Cun-jun1, ZHU Da-zhou1, HUANG Wen-jiang1, LI Yu-fei3, YANG Xiao-dong1* . Using Extraction of Red Edge Position to Validate Consistency of Hyperspectral Imaging and Non-Imaging Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(09): 2450-2454. |
| [6] |
ZHANG Dong-yan1, 2, LIU Rong-yuan2, 3, SONG Xiao-yu2, XU Xin-gang2, HUANG Wen-jiang2, ZHU Da-zhou2, WANG Ji-hua1, 2*. A Field-Based Pushbroom Imaging Spectrometer for Estimating Chlorophyll Content of Maize[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(03): 771-775. |
|
|
|
|
