An intelligent divisional green tide detection of adaptive threshold for GF-1 image based on data mining
-
摘要: 由于受到云雾的影响,可见光影像能够高效用于绿潮检测的数据源较为有限,特别是云覆盖较为严重的可见光影像,基本无法用于检测绿潮。即使影像数据是在薄云、薄雾、无云覆盖的情况下获取的,由于其光谱反射值存在较大差异,依然很难采用同一阈值进行绿潮检测。基于此,为了提高可见光影像的利用率,实现不同云覆盖情况下,绿潮的高精度自适应阈值的自动检测,本文以GF-1影像为数据源,首先采用K-means聚类和C4.5决策树方法实现影像云覆盖情况的自动识别;其次,选取大量不同云覆盖情况下子图像样本(每个子图像样本中均包含绿潮和海水两类),分析得出不同云覆盖情况下绿潮和海水的区分阈值y与影像光谱差x=bandnir-bandred之间所具有的线性关系;然后,利用分析得出的线性关系提出一种适用于GF-1影像的绿潮分区自适应阈值自动检测方法。最后,为验证提出方法的有效性,分别采用NDVI方法、EVI方法和本文提出的自适应阈值自动检测方法进行绿潮提取实验。实验结果表明,对于GF-1卫星遥感数据,本文提出的绿潮自适应阈值分区自动检测方法明显优于传统的NDVI和EVI检测方法,不仅提高了绿潮的监测精度,而且实现了绿潮提取的全自动化。Abstract: Due to the influence of clouds, the visible light images that can be used effectively for green tide detection are limited, especially when the cloud coverage is serious, which can not be used to detect green tide. Even if the image data is acquired under thin cloud, mist, and cloudless coverage, it is still difficult to use the same threshold for green tide detection because of the large difference in spectral reflectance values. Based on this, in order to improve the utilization of visible light image and realize green tide high-precision automatic detection of the adaptive threshold under different cloud coverage conditions, GF-1 images are selected as data source, firstly, K-means clustering and C4.5 decision tree methods are used to automatically identify cloud coverage type; secondly, a large number of sub-image samples with different cloud coverage are selected (each sub-image sample contains two types of green tide and sea water), and the linear relationship between the classification threshold y and the image spectral difference x (x = bandnir-bandred) is analyzed under different cloud coverage, here, the classification threshold y is the value that can distinguish green tide and sea; then, green tide partition adaptive threshold automatic detection method for GF-1 image is proposed by using the linear relationship analyzed. Finally, in order to verify the effectiveness of the proposed method, NDVI、EVI methods and the adaptive threshold automatic detection method proposed in this paper are used to carry out the green tide extraction experiment. The experimental results show that for the GF-1 satellite remote sensing data, the green tide adaptive threshold partition automatic detection method is better than traditional NDVI and EVI methods, which not only improves the monitoring accuracy of green tide, but also realizes the full automation of green tide extraction.
-
Key words:
- GF-1 /
- green tide /
- K-means algorithm /
- C4.5 decision tree algorithm /
- adaptive threshold
-
陈群芳, 何培民, 冯子慧, 等. 漂浮绿潮藻浒苔孢子/配子的繁殖过程[J]. 中国水产科学, 2011, 18(5):1069-1076. Chen Qunfang, He Peimin, Feng Zihui, et al. Reproduction of spores/gametes of floating green tide algae Ulva prolifera[J]. Journal of Fishery Sciences of China, 2011, 18(5):1069-1076. Miao Xiaoxiang, Xiao Jie, Pang Min, et al. Effect of the large-scale green tide on the species succession of green macroalgal micro-propagules in the coastal waters of Qingdao, China[J]. Marine Pollution Bulletin, 2018, 126:549-556. 张永梅, 潘振宽, 曹丛华, 等. 基于变分水平集方法的浒苔绿潮面积信息提取[J]. 海洋学报, 2017, 39(9):121-132. Zhang Yongmei, Pan Zhenkuan, Cao Conghua, et al. Information extraction of enteromorpha green tide area based on variational level set method[J]. Haiyang Xuebao, 2017, 39(9):121-132. 张海龙, 孙德勇, 李俊生, 等. 基于GF1-WFV和HJ-CCD数据的我国近海绿潮遥感监测算法研究[J]. 光学学报, 2016, 36(6):601004. Zhang Hailong, Sun Deyong, Li Junsheng, et al. Remote sensing algorithm for detecting green tide in China coastal waters based on GF1-WFV and HJ-CCD data[J]. Acta Optica Sinica, 2016, 36(6):601004. 王宗灵, 傅明珠, 肖洁, 等. 黄海浒苔绿潮研究进展[J]. 海洋学报, 2018, 40(2):1-13. Wang Zongling, Fu Mingzhu, Xiao Jie, et al. Progress on the study of the Yellow Sea green tides caused by Ulva prolifera[J]. Haiyang Xuebao, 2018, 40(2):1-13. 迟丽宁, 邵峰晶, 王常颖, 等. 基于关联规则的MODIS影像绿潮检测[J]. 青岛大学学报(自然科学版), 2012, 25(2):58-61. Chi Lining, Shao Fengjing, Wang Changying, et al. MODIS images based on association rules green tide monitoring[J]. Journal of Qingdao University (Natural Science Edition), 2012, 25(2):58-61. Hu Chuanmin. A novel ocean color index to detect floating algae in the global oceans[J]. Remote Sensing of Environment, 2009, 113(10):2118-2129. Keesing J K, Liu Dongyan, Fearns P, Garcia R. Inter-and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007-2009, their origin and relationship to the expansion of coastal seaweed aquaculture in China[J]. Marine Pollution Bulletin, 2011, 62:1169-1182. Son Y B, Min J E, Ryu J H. Detecting massive green algae (Ulva prolifera) blooms in the Yellow Sea and East China Sea using Geostationary Ocean Color Imager (GOCI) data[J]. Ocean Science Journal, 2012, 47(3):359-375. Zhang Jianheng, Huo Yuanzi, Zhang Zhenglong, et al. Variations of morphology and photosynthetic performances of Ulva prolifera during the whole green tide blooming process in the Yellow Sea[J]. Marine Environmental Research, 2013, 92:35-42. Xing Qianguo, Tosi L, Braga F, et al. Interpreting the progressive eutrophication behind the world's largest macroalgal blooms with water quality and ocean color data[J]. Natural Hazards, 2015, 78(1):7-21. 杨静, 张思, 刘桂梅. 基于卫星遥感监测的2011-2016年黄海绿潮变化特征分析[J]. 海洋预报, 2017, 34(3):56-61. Yang Jing, Zhang Si, Liu Guimei. Variability analysis of the Green Tide based on satellite remote sensing monitoring data from 2011 to 2016 in the Yellow Sea[J]. Marine Forecasts, 2017, 34(3):56-61. 周雪珺, 杨晓非, 姚行中. 遥感图像的云分类和云检测技术研究[J]. 图学学报,2014, 35(5):768-773. Zhou Xuejun, Yang Xiaofei, Yao Xingzhong. The study of cloud classification and detection in remote sensing image[J]. Journal of Graphics, 2014, 35(5):768-773. 丘仲锋, 崔廷伟, 何宜军. 基于水体光谱特性的赤潮分布信息MODIS遥感提取[J]. 光谱学与光谱分析, 2011, 31(8):2233-2237. Qiu Zhongfeng, Cui Tingwei, He Yijun. Retrieve of red tide distributions from MODIS data based on the characteristics of water spectrum[J]. Spectroscopy and Spectral Analysis, 2011, 31(8):2233-2237. 肖艳芳, 张杰, 崔廷伟, 等. 海面漂浮绿潮生物量光谱特征及估算模型[J]. 光学学报, 2017, 37(4):430001. Xiao Yanfang, Zhang Jie, Cui Tingwei, et al. Spectral characteristics and estimation models of floating green tide biomass on sea surface[J]. Acta Optica Sinica, 2017, 37(4):430001. 杨旭. 遥感影像的自适应阈值法水陆分割研究[J]. 科技资讯, 2013(5):42-43. Yang Xu. Research on water-land segmentation of remote sensing image based on adaptive threshold method[J]. Science and Technology Information, 2013(5):42-43. 米雪婷, 孙林, 韦晶,等. 基于多时相遥感数据的云阴影检测算法[J]. 山东科技大学学报(自然科学版), 2016, 35(2):64-72. Mi Xueting, Sun Lin, Wei Jing, et al. Cloud shadow detection algorithm based on multi-temporal remote sensing data[J]. Journal of Shandong University of Science and Technology (Natural Science), 2016, 35(2):64-72. 李炳燮, 马张宝, 齐清文,等. Landsat TM遥感影像中厚云和阴影去除[J]. 遥感学报, 2010, 14(3):534-545. Ri Pyongsop, Ma Zhangbao, Qi Qingwen, et al. Cloud and shadow removal from Landsat TM data[J]. Journal of Remote Sensing. 14(3):534-545. 慕娟,杜超本,易洲. 遥感图像的NSCT自适应阈值去噪方法[J].无线电工程,2012,42(11):23-25. Mu Juan, Du Chaoben, Yi Zhou. Adaptive threshold for remote sensing image denoising based on wavelet and NSCT[J]. Radio Engineering, 2012,42(11):23-25. Wang Changying, Chu Jialan, Tan Meng, et al. An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image[J]. Acta Oceanologica Sinica, 2017, 36(11):106-114.
点击查看大图
计量
- 文章访问数: 619
- HTML全文浏览量: 26
- PDF下载量: 273
- 被引次数: 0