基于引力模型的海洋锋信息提取
收稿日期: 2012-11-19
修回日期: 2013-01-07
网络出版日期: 2013-04-18
基金资助
国家科技支撑课题“小卫星智能观测荒漠化和海岸带监测应用示范”(2011BAH23B04);国家海洋公益性行业科研专项经费资助项目(201005011)。
Application of the Model of Universal Gravity to Oceanic Front Detection Near the Kuroshio Front
Received date: 2012-11-19
Revised date: 2013-01-07
Online published: 2013-04-18
海洋锋面是海洋水团特性明显不同的两种或几种水体之间的狭窄过渡带。本文旨在对遥感反演海洋温度场数据(SST), 引入引力模型进行海洋锋面的检测。鉴于海洋锋受噪声干扰大, 锋面强度小的特点, 本文提出了基于引力算法的引力模型。其中, 引力算法是将温度数据中的每一个像元点都作为一个独立的天体, 其质量对应该像元的温度值, 根据引力定律计算3×3 区域中, 邻域像元对中心点像元的引力和。模型首先对原始数据进行去0 处理, 为消除对原始数据明暗程度的依赖, 对3×3 区域数据进行归一化, 然后利用函数对归一化后的数据进行增强处理, 最后, 以引力算法进行锋面检测。验证表明, 该模型能有效强化不同区域或水体差异性, 并能够有效针对海洋锋信息进行提取, 受噪声影响小。
平博, 苏奋振, 杜云艳, 孟云闪, 苏伟光 . 基于引力模型的海洋锋信息提取[J]. 地球信息科学学报, 2013 , 15(2) : 187 -192 . DOI: 10.3724/SP.J.1047.2013.00187
Oceanic front is a narrow transitional zone that the penetration of sea is obviously different between two or more waters there, and it plays an important role in the national production, national defense, marine and weather. Based on the modified theory of universal gravity, sea surface temperature (SST) data near the Kuroshio front are used for front detection. The theory of universal gravity assumes that each image pixel is a celestial body with a mass represented by its value. According to the law of universal gravity, the forces of the pixels in the 3 × 3 neighbourhood exerted on the central pixels can be calculated. Because fronts are susceptible to the noise and intense of fronts are commonly low, a modified method are proposed to solve these problems in this article. This method firstly eliminates the pixels that values equal to 0. Then in order to decrease the reliance on the brightness level of original data, a normalization step is applied to each 3×3 neighbourhood and next based on image enhancement function, each normalized 3×3 area can be enhanced. Finally, the theory of universal gravity is applied to enhanced data for front detection. The algorithm was tested and compared with conventional methods using in the fronts detection such as Sobel, Jensen-Shannon. The results show that compared to conventional methods in some areas, the proposed algorithm can decrease noise while not cause fronts discontinuous.
Key words： SST; the theory of universal gravity; oceanic fronts; edge detection
[1] 李凤岐,苏育嵩.海洋水团分析[M].青岛:青岛海洋大学出版社,2000.
[2] Cayula J F, Cornillon P. Edge detection algorithm for sstimages[J]. Journal of Atmospheric and Oceanic Technology,1992,9(1):67-80.
[3] Cornillon P. Multi-image edge detection for sst images[J].Journal of Atmospheric and Oceanic Technology,1995,12(1):821:829.
[4] Cayula J F, Cornillon P, Holyer P, et al. Comparative studyof two recent edge-detection algorithms designed to processsea-surface temperature fields[J]. IEEE Transactionson Geoscience and Remote Sensing,, 1991,29(1):175-177.
[5] Ullman D S, Cornillon P C. Evaluation of front detectionmethods for satellite-derived sst data using in situ observations[J]. Journal of Atmospheric and Oceanic Technology,2000,17(12):1667-1675.
[6] Miller P. Multi-spectral front maps for automatic detectionof ocean colour features from seawifs[J]. InternationalJournal of Remote Sensing, 2004,25(7-8):1437-1442.
[7] Diehl S F, Budd J W, Ullman D, et al. Geographic windowsizes applied to remote sensing sea surface temperaturefront detection[J]. Journal of Atmospheric and OceanicTechnology, 2002,19(7):1105-1113.
[8] Vázquez D P, Atae-Allah C, Escamilla P L L. Entropic approachto edge detection for sst images[J]. Journal of Atmosphericand Oceanic Technology,1999,16(7):970-979.
[9] Shimada T, Sakaida F, Kawamura H, et al. Application ofan edge detection method to satellite images for distinguishingsea surface temperature fronts near the Japanese coast[J]. Remote Sensing of Environment, 2005,98(1):21-34.
[10] Castelao R M, Mavor T P, Barth J A, et al. Sea surfacetemperature fronts in the california current system fromgeostationary satellite observations[J]. Journal of GeophysicalResearch Oceans, 2006(111):C09026(1-13).
[11] 石汉青,张伟,尹志泉.基于Canny 算子和数学形态学的海洋锋检测方法研究[C]. 2010 年国际遥感大会(ICRS2010), 杭州, 2010年10 月.
[12] Oram J J, Mcwilliams J C, Stolzenbach K D. Gradient-based edge detection and feature classification ofsea-surface images of the southern california bight[J]. RemoteSensing of Environment, 2008,112(5):2397-2415.
[13] 尹劲峰,刘仁义,刘南.SST 图像温锋提取算法研究[J].计算机应用研究,2005,22(2):225-226.
[14] 薛存金, 苏奋振, 周军其,等.基于形态学的海洋锋形态特征提取[J].海洋科学,2008,32(5):57-61.
[15] Sun G Y, Liu Q H, Liu Q, et al. A novel approach foredge detection based on the theory of universal gravity[J]. Pattern Recognition, 2007(40): 2766-2775.
[16] 张春雪,陈秀宏.基于非线性滤波的万有引力边缘检测方法[J].计算机应用,2011,31(3):763-766.
[17] Lopez-Molina C, Bustince H, Fernandez J, et al. A gravitationalapproach to edge detection based on triangularnorms[J]. Pattern Recognition, 2010,43(2010):3730-3741.
[18] Belkin I M, O'Reilly J E. An algorithm for oceanic frontdetection in chlorophyll and SST satellite imagery[J].Journal of Marine Systems, 2009(78):319-326.
[19] Rivas A L, Pisoni J P. Identification, characteristics andseasonal evolution of surface thermal fronts in the ArgentineanContinental Shelf[J]. Journal of Marine Systems,2010(9):134-143.
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