Spectral fidelity and water depth remote sensing detection of EMD of GF-1 WFV images

Chen Chen; Ma Yi; Zhang Jingyu

doi:10.3969/j.issn.0253-4193.2018.04.005

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Chen Chen, Ma Yi, Zhang Jingyu. Spectral fidelity and water depth remote sensing detection of EMD of GF-1 WFV images[J]. Haiyang Xuebao, 2018, 40(4): 51-60. doi: 10.3969/j.issn.0253-4193.2018.04.005

Citation:

Chen Chen, Ma Yi, Zhang Jingyu. Spectral fidelity and water depth remote sensing detection of EMD of GF-1 WFV images[J]. Haiyang Xuebao, 2018, 40(4): 51-60. doi: 10.3969/j.issn.0253-4193.2018.04.005

Chen Chen, Ma Yi, Zhang Jingyu. Spectral fidelity and water depth remote sensing detection of EMD of GF-1 WFV images[J]. Haiyang Xuebao, 2018, 40(4): 51-60. doi: 10.3969/j.issn.0253-4193.2018.04.005

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Spectral fidelity and water depth remote sensing detection of EMD of GF-1 WFV images

doi: 10.3969/j.issn.0253-4193.2018.04.005

First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China

Received Date: 2017-04-14
Rev Recd Date: 2017-10-16

Abstract

Abstract

Water depth is one of the important parameters of the marine environment,and water depth remote sensing is an important means of water depth measurement. EMD can eliminate small-scale wave information,leaving large-scale underwater terrain information. The paper uses the EMD to scale the GF-1 WFV image. The spectral fidelity analysis of the remaining layer images was carried out by using spectral correlation coefficient,spectral angle mapper,spectral error and spectral relative error. The paper uses the improved logarithmic transformation ratio model to carry out the water depth inversion of the original image and the remaining layer image,and carry on the correlation analysis and the accuracy evaluation. Research indicates:(1)The evaluation index shows that the image has considerable spectral fidelity after the EMD transformation. Analysis of spatial section shows that the EMD removes the small-scale noise information and retains the underwater terrain change information. (2)The uniform distribution of the checkpoints to verify that,the correlation between the depth of the original image and the measured water depth is better the correlation coefficient is above 0.75,and the MAE and MRE of the two kinds of band combination are not more than 2.42 m and 8.5%. (3)A water depth inversion was performed on all 10 layers of EMD remaining layer. The MAE and MRE of the combination of blue and green bands are not higher than 1.62 m and 5.8%. The MAE and MRE of the combination of green and red bands are no more than 1.93 m and 6.9%. (4) For different combinations of bands,the effect of blue-green band combination in the remaining layers is superior to the green-red band,and the water depth inversion accuracy is improved significantly after EMD. (5)The inversion accuracy of 20-30 m water depth is higher than 30-40 m,which indicates that the model is more suitable for shallow water depth.
- GF-1 WVF,
- EMD,
- spectral fidelity,
- water depth remote sensing

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References

田震, 马毅, 张靖宇, 等. 基于Landsat-8遥感影像和LiDAR测深数据的水深主被动遥感反演研究[J]. 海洋技术学报, 2015(2):1-8. Tian Zhen, Ma Yi, Zhang Jingyu, et al. Study on the bathymetry inversion by active and passive remote sensing with Landsat-8 images and LiDAR data[J]. Journal of Ocean Technology, 2015(2):1-8.

Stumpf R P, Holderied K, Sinclair M. Determination of water depth with high-resolution satellite imagery over variable bottom types[J]. Limnology and Oceanography, 2003, 48(1):547-556.

田震. 浅海水深多/高光谱遥感模型与水深地形图制作技术研究[D]. 青岛:山东科技大学, 2015. Tian Zhen. Study on bathymetry inversion models using multispectral or hyperspectral data and bathyorographical mapping technology[D]. Qingdao:Shandong University of Science and Technology, 2015.

Huang N E, Zheng S, Steven R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear non-stationary time Series Analysis[J]. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995.

宋平舰, 张杰. 二维经验模分解在海洋遥感图像信息分离中的应用[J]. 高技术通讯, 2001, 11(9):62-67. Song Pingjian, Zhang Jie. The application of two-dimensional EMD to separating contents of oceanic remote sensing images[J]. High Technology Letters, 2001, 11(9):62-67.

Nunes J C, Guyot S, Deléchelle E, et al. Texture analysis based on local analysis of the Bidimensional Empirical Mode Decomposition[J]. Machine Vision and Applications, 2005, 16(3):177-188.

刘锟, 付晶莹, 李飞. 高分一号卫星4种融合方法评价[J]. 遥感技术与应用, 2015, 30(5):980-986. Liu Kun, Fu Jingying, Li Fei. Evaluation Study of four fusion methods of GF-1 PAN and multi-spectral images[J]. Remote Sensing Technology and Application, 2015, 30(5):980-986.

崔廷伟, 张杰, 马毅, 等. 渤海悬浮物分布的遥感研究[J]. 海洋学报, 2009, 31(5):10-18. Cui Tingwei, Zhang Jie, Ma Yi, et al. The study on the distribution of suspended particulate matter in the Bohai Sea by remote sensing[J]. Haiyang Xuebao, 2009, 31(5):10-18.

周欣. 二维经验模式分解(BEMD)在图像处理中的应用[D]. 武汉:华中科技大学, 2007. Zhou Xin. Application of BEMD method in image processing[D]. Wuhan:Huazhong University of Science and Technology, 2007.

Wang Jian, Zhang Jixian, Liu Zhengjun. EMD based multi-scale model for high resolution image fusion[J]. Geo-Spatial Information Science, 2008, 11(1):31-37.

安妮, 马毅, 包玉海. 基于经验模分解高光谱图像数据尺度变换的光谱保真性分析[J]. 遥感技术与应用, 2016, 31(2):230-238. An Ni, Ma Yi, Bao Yuhai. Spectral fidelity analysis of scaling transformation of hyperspectral remote sensing image based on empirical mode decomposition[J]. Remote Sensing Technology and Application, 2016, 31(2):230-238.

周振国. 二维EMD方法及其在图像处理中的应用研究[D]. 哈尔滨:哈尔滨工程大学, 2012. Zhou Zhenguo. Two-dimensional EMD method and its application in image processing[D]. Harbin:Harbin Engineering University, 2012.

贺璐璐. 二维经验模式分解及其在图像分析中的应用[D]. 武汉:华中科技大学, 2007. He Lulu. Bidimensional Empirical Mode Decomposition and its application in image analysis[D]. Wuhan:Huazhong University of Science and Technology, 2007.

庄展鹏. 二维经验模分解研究及其在合成孔径雷达图像分析中的应用[D]. 青岛:中国海洋大学, 2013. Zhuang Zhanpeng. Research on the Bi-dimensional Empirical Mode Decomposition and application to the analysis of Synthetic Aperture Radar image[D]. Qingdao:Ocean University of China, 2013.

郑中, 祁元, 张金龙. 基于光谱角和光谱距离评价指标的遥感影像融合方法比较研究——以QuickBird数据为例[J]. 遥感技术与应用, 2013, 28(3):437-443. Zheng Zhong, Qi Yuan, Zhang Jinlong. Comparing with different remote sensing image fusion method based on evaluation index of spectral angle and spectral distance-taking QuickBird data as example[J]. Remote Sensing Technology and Application, 2013, 28(3):437-443.

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