基于遗传自组织神经元网络的可见光与热红外遥感数据融合方法
Fusion of Visible and Thermal Infrared Remote Sensing Data Based on GA-SOFM Neural Network
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摘要: 提出了一种融合方法,充分发掘可见光数据高空间分辨率和热红外数据高时间分辨率的特点,在由可见光数据估算的地表参量与热红外数据间,通过遗传自组织神经元网络建立非线性融合方法,最终获得高空间、高时间分辨率的地表温度数据,并利用ASTER卫星产品数据对该方法进行了验证。结果表明,该方法简便易行,精度较高,为快速获取高分辨率地表温度分布提供了一条新途径。Abstract: An approach which can take advantage of high spatial resolution feature of visible data and high temporal resolution feature of thermal infrared data,is adapted by a nonlinear fusion method based on GA-SOFM-ANN to map the relation between retrieved land surface parameters from visible data and temperature.According to this method,a result of fusing both visible data with spatial resolution feature and thermal infrared data with temporal feature is finished.A case of testing method is showed,utilizing the ASTER data.The conclusions show that it is a new approach to quickly estimate and acquire high resolution land surface temperature.
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