湖泊叶绿素高光谱空谱联合遥感反演

高光谱遥感提供的精细光谱信息给水色遥感参数反演提供了广阔的前景,然而光谱分辨率高但空间分辨率较低的特点,使得目前的高光谱水色遥感反演模型和算法普遍缺乏对空间信息的有效利用,模型的精度和稳定性往往难以保证。以巢湖为研究区,利用HJ-1A卫星HSI高光谱遥感数据,结合地面实测样点数据,在深入分析叶绿素光谱特性基础上构建基于空间八邻域与遗传算法的水体叶绿素a高光谱遥感反演模型, 并以matlab7.0为平台,联合光谱指数与遗传算法求解叶绿素a浓度反演模型参数,经空间邻域分析与遗传迭代,求出叶绿素浓度最优解。结果表明,遗传算法摒弃了传统的搜索方式,以光谱信息为基础,在邻近空间域上采用模拟进化方式对水色空间进行随机优化搜索, 跳出了局部极值点,能够有效提高模型反演的精度。

Abstract

Fine spectral information of hyper-spectral remote sensing provides a good prospect for parameters of water color inversion by remote sensing. However, for high spectral resolution and low spatial resolution, the current hyper-spectral remote sensing inversion models and algorithms of water color are generally lack of effective use of spatial information, so it is difficult to ensure accuracy and stability of the model. Taking Chaohu Lake, Anhui, China, as the study area, based on the space eight neighborhoods and genetic algorithm, hyper-spectral remote sensing HSI data of HJ-1A satellite is combined with the measured sample data to construct a hyper-spectral remote sensing inversion model of chlorophyll by the in-depth analysis of the spectral characteristics of water body. Based on matlab7.0 platform, the parameters of inversion model is calculated by the combined spectral indices and genetic algorithm. Under the spatial neighborhood analysis and genetic iteration, the concentration of chlorophyll is solved. The results show that the genetic algorithm abandons the traditional search methods, optimizes and searches water color space randomly using simulated evolution in the vicinity of the spatial domain by spectral information, jumps out of the local extreme point, can effectively improve the accuracy of model inversion.

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潘邦龙, 申慧彦, 邵慧, 李卫华. 湖泊叶绿素高光谱空谱联合遥感反演[J]. 大气与环境光学学报, 2017, 12(6): 428. PAN Banglong, SHEN Huiyan, SHAO Hui, LI Weihua. Combined Inversion of Hyper-Spectral Remote Sensing of Space and Spectrum for Lake Chlorophyll[J]. Journal of Atmospheric and Environmental Optics, 2017, 12(6): 428.

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