基于改进的亚像元分解方法的高光谱海岸瞬时水边线提取

海岸线是多年平均大潮高潮所形成的海水和陆地分界线的痕迹线,遥感技术可以提供大范围的海岸线动态监测。传统的硬分类方法提取海岸瞬时水边线是基于像元级的基础上进行的,其提取的精度较低;然而利用亚像元分解方法在复杂海岸地带上提取海岸瞬时水边线,是一项既新颖又具有挑战性的任务。因此,提出一种改进的亚像元海岸瞬时水边线提取方法(Improved Sub-pixel Coastal Waterline,ISPCW)可以获得较高的海岸瞬时水边线提取精度。首先,使用了一种水体-植被-不透水层-土壤模型(Water-VegetationImpervious-Soil,W-V-I-S)用于检测和确定海岸地带的W-V-I-S混合像元和纯净端元光谱;随后使用全约束最小二乘法(Fully Constrained Least Squares,FCLS)估计W-V-I-S混合像元中水体丰度值;最后使用空间吸引力模型获得海岸瞬时水边线。在上海实验区中,采用EO-1高光谱数据,将ISPCW方法和传统的多端元光谱混合分析(Multiple Endmember Spectral Mixture Analysis,MESMA)、混合调谐匹配滤波法(Mixture Tuned Matched Filtering,MTMF)、连续最大角凸锥(Sequential Maximum Angle Convex Cone,SMACC)、能量约束最小化(Constrained Energy Minimization,CEM)混合像元分解方法和归一化水体指数(Normalized Difference Water Index,NDWI)进行对比。实验结果表明,ISPCW方法用于提取海岸瞬时水边线获得较好的效果,其精度达到0.38个像元,与MESMA、MTMF、SMACC、CEM和NDWI方法相比,精度分别提高了22.4%、33.3%、42.4%、43.2%和51.3%,可以更有效的应用于高光谱海岸瞬时水边线提取。

Coastal waterline extraction based on an improved sub-pixel unmixing method using EO-1 Hyperion data

Shoreline is described as an intersection of coastal land and water surface indicating water edge movements as the tides rise and fall. Remote sensing technology can provide a wide range dynamicmonitoring of the shoreline. However, traditional hard classification methods are mainly used to extract coastal waterline at the pixel level, and achieve the low accuracy. Whereas sub-pixel coastal waterline extraction is an attractive and challenging task due to the complex features in the coastal region. Therefore, an improved sub-pixel coastal waterline extraction method (ISPCW) is presented to achieve the higher accuracy of coastal waterline extraction. Firstly, a Water-Vegetation-Impervious-Soil (W-V-I-S) model is presented to detect W-V-I-S mixed pixels and determine endmember spectrum in the coastal region. Secondly, the linear spectral mixture unmixing technique based on Fully Constrained Least Squares (FCLS) is applied to the W-V-I-S mixed pixels for water abundance estimation; and finally, spatial attraction model is used to extract coastal waterline. In the experiment performed on EO-1 Hyperion data of Shanghai study area, Multiple Endmember Spectral Mixture Analysis (MESMA), Mixture Tuned Matched Filtering (MTMF), Sequential Maximum Angle Convex Cone (SMACC), and Constrained Energy Minimization (CEM), and classical Normalized Difference Water Index (NDWI) methods are chosen for the coastal waterline extraction comparison. The results indicate that the proposed ISPCW method achieved the best accuracy of 0.38 pixels in the experiment, and the accuracy of ISPCW method improved by 22.4%,33.3%,42.4%, 43.2%, and 51.3%compared with MESMA, MTMF, SMACC, CEM, and NDWI methods, respectively. Therefore, from these results, the ISPCW method exhibits better performance for coastal waterline extraction than the traditional pixel level method and sub-pixel level method, and can be effectively applied to coastal waterline extraction in the coastal region.