Mixture End-Members Extraction Method For Coverage Calculation of Sea Oil Spills Based on Hyper-spectral Remote Sensing Images
HAN Zhong-zhi1, WANG Xuan-hui1,2, SHI Hong-tao1, WAN Jian-hua3*
1. Information College, Qingdao Agricultural University, Qingdao 266109, China
2. Key Lab of Marine Environmental Science and Ecology, Ministry of Education, College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China
3. School of Geosciences, China University of Petroleum, Qingdao 266580, China
Abstract：How to estimate the coverage rate of oil spills is an important part of the sea oil spills detection. To be limited by spatial resolution of airborne hyperspectral remote sensing image, it is difficult to detect the coverage of oil spills accurately. Under the action of ocean waves and broken waves, the oil spill tends to be banded distribution. Because there are a lot of oil and water mixed pixels in the hyperspectral data, the traditional image segmentation method which is used to calculate the oil spill area was mistaken in many ways. It is difficult to find the pure spectral spectrum because the traditional extraction method is influenced by the angle and height of the sensor and the spectral variation. In this paper, we proposed a second extraction method of endmembers. Firstly, N-FINDR algorithm is used for the endmembers’ preselection. Secondly, the Independent Component Analysis (ICA) is used for the ultimate refinement of endmembers’ selection and the candidate endmembers are obtained according to the maximum output of negative entropy. Thirdly, the ground synchronous reference spectra are used as constraints to identify the similar oil spill terminals. Finally, the end members’ abundances are obtained based on the nonnegative matrix decomposition method (NMF) and the real oil spill coverage are obtained through correction of solar flare region. The extraction of partitioned mixed endmembers is a good solution to the problem of global endmembers mutation and poor environment adaptability, so that the selected endmembers have better environment robustness. In order to evaluate the accuracy of this algorithm, the simulation data and the real hyperspectral image data are combined to verify the experiment. In the simulation experiment, the difference between the estimated abundance and the set abundance are evaluated by using the mean square error (RMSE) and the abundance estimation error, the algorithm adaptability and anti-noise experiment are designed. The result indicated that, under two hyper-spectral compression case by MNF and PCA, estimation error of abundance is less than 3%. The minimum RMSE of reconstructed image is up to 0.030 6 and has good anti-noise ability. The accuracy evaluation results verify the effectiveness of the proposed algorithm. In the real experiment, 8 hyper-spectral remote sensing image collected by airborne of Shandong Changdao in 2011 are used for real test data. Because the real remote sensing data often lacks the ground synchronization abundance data, it is difficult to evaluate the accuracy of the algorithm. The combination of simulation data with verification and visual interpretation data are used to evaluate the accuracy. The total oil spill coverage area of airborne hyperspectral imaging estimated by the flare area is 1.17 km2, the oil spill coverage is 22.85%, and the field artificial estimation area deviation is 2.15%. Obviously the method is superior to the traditional method. It is difficult to analyze the abundance of single pixel in ocean oil spill remote sensing because it is influenced by the ocean breaking wave, spectral variability and the limitation of ground resolution of aerospace remote sensor. Based on the idea of the abundance decomposition of the image, this paper discusses the problem of the coverage of ocean oil spill, and puts forward a comparatively perfect method for calculating the covering degree of ocean oil spill. The method is validated through the simulation data and the actual hyperspectral oil spill data. The method is an objective automatic oil spill coverage (abundance) detection method and could realize the automatic monitoring of oil spill coverage rate. It is meaning for fine detection of oil spills area.
韩仲志，王轩慧，时鸿涛，万剑华. 高光谱遥感分区混合端元提取计算海洋溢油覆盖度[J]. 光谱学与光谱分析, 2019, 39(05): 1563-1570.
HAN Zhong-zhi, WANG Xuan-hui, SHI Hong-tao, WAN Jian-hua. Mixture End-Members Extraction Method For Coverage Calculation of Sea Oil Spills Based on Hyper-spectral Remote Sensing Images. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1563-1570.
[1] Kessle J D, Valentine D L, Redmond M C, et al. Science, 2011, 331(6015): 312.
[2] Lu Yingcheng, Li Xiang, Tian Qingjiu, et al. Marine Geodesy, 2013, 36 (3): 34.
[3] WANG Xuan-hui, ZHENG Xi-lai，HAN Zhong-zhi, et al(王轩慧，郑西来，韩仲志，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(12): 3883.
[4] Rrmsey III E, Rangoonwala A, Suzuoki Y, et al. Remote Sensing, 2011, 3(12)：2630.
[5] Lennon M, Babichenko S, Thomas N, et al. Earsel Eproceedings，2006, 5(1): 120.
[6] Cococcioni M, Corucci L, Masini A, et al. Ocean Dynamics, 2012, 62(3): 449.
[7] Srivastava H, Singh T P. Journal of the Indian Society of Remote Sensing, 2010, 38(3): 161.
[8] LIU Bing-xin, ZHANG Zhi-da, LI Ying, et al(刘丙新, 张志达, 李 颖, 等). Journal of Dalian Maritime University（大连海事大学学报）, 2014, 40(1): 89.
[9] GUO Chun-lei, XIE Wei-jia, HUANG Hua-guo(郭春蕾, 解潍嘉, 黄华国). Journal of Northeast Forestry University(东北农业大学学报), 2014, 42(11): 82.
[10] Keshava N, Mustard J F. Signal Processing Magazine, IEEE, 2002, 19(1): 44.
[11] Winter M E. International Society for Optics and Photonics, 1999，3870：150.
[12] Hyvärinen A, Karhunen J, Oja E. Independent Component Analysis. Wiley and Sons. 2001
[13] Hyvärinen A, Oja E. Neural Computation, 1997，9(7)：1483.
[14] Lee D D, Seung H S. Nature, 1999, 401(21): 788.
[15] Zhang Zhou, Shi Zhenwei. Neural Computing and Applications, 2013, 23 (3): 895.
[16] LIU Wan-jun, YANG Xiu-hong, QU Hai-cheng, et al(刘万军, 杨秀红, 曲海成, 等). Journal of Computer Applications(计算机应用), 2015, 35(3): 844.
[17] ZHANG Xiang, ZHANG Jian-qi, JIN Wei, et al(张 翔, 张建奇, 靳 薇, 等). Acta Optica Sinica(光学学报), 2008, 28(4): 664.
[18] Han Zhongzhi, Wan Jianhua, Zhang Jie, et al. Chinese Journal of Oceanology and Limnology, 2017, 35(4): 978.
[19] The Environmental Monitoring Center of North China Sea, GB/T 21247—2007. Specifications for Identification System of Spilled Oils on the Sea(国家海洋局北海环境监测中心. GB/T 21247—2007. 海面溢油鉴别系统规范)，2007.
