GF-1 WFV图像经验模分解的光谱保真性与水深遥感探测

陈琛; 马毅; 张靖宇

doi:10.3969/j.issn.0253-4193.2018.04.005

GF-1 WFV图像经验模分解的光谱保真性与水深遥感探测

doi: 10.3969/j.issn.0253-4193.2018.04.005

国家海洋局第一海洋研究所, 山东青岛 266061

基金项目: 国家重点研发计划（2016YFC1402701）；高分辨率对地观测系统重大专项（民用部分）应用示范系统项目——高分海岸带遥感监测与应用示范；国家科技支撑计划项目——远海岛礁地理信息监测关键技术研究与示范（2012BAB16B01-02）。

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出版历程
- 收稿日期: 2017-04-14
- 修回日期: 2017-10-16

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

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

摘要

摘要: 水深是海洋环境的重要参数之一，水深遥感反演是水深测量的一种重要手段。经验模分解（EMD）具有剔除小尺度波浪信息，留下大尺度水下地形信息的特性。本文利用EMD对高分一号卫星宽幅影像进行尺度变换，使用光谱相关系数、光谱角、光谱偏差和光谱相对偏差等评价指标，对剩余层图像进行光谱保真性分析；利用改进的对数转换比值模型对原始影像和剩余层图像进行水深反演，并进行相关性分析与精度评价。研究结果表明：（1）评价指标显示EMD变换后影像具有相当的保真性；空间断面分析表明EMD去除了小尺度的噪声信息，保留了水下地形变化信息。（2）经均匀分布的检查点验证，两区域的原图像反演水深和实测水深的相关性较好，相关系数达0.75以上，且两种波段组合的MAE和MRE均不超过2.42 m和8.5%。（3）对EMD的全部10层进行水深反演，蓝绿波段的MAE和MRE均不高于1.62 m和5.8%；绿红波段的MAE和MRE均不高于1.93 m和6.9%。（4）对于不同的波段组合，蓝绿波段组合在各剩余层的水深反演效果明显优于绿红波段，经EMD后的水深反演效果明显提高。（5）20~30 m水深段的反演精度整体要高于30~40 m，该模型应用于较浅水深段更具优势。
- GF-1WVF /
- EMD /
- 光谱保真性 /
- 水深遥感
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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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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