基于光谱纹理特征融合和神经网络的地表发射率获取方法（英文）

Retrieval of land surface emissivity using spectral and texture features based on neural network

地表发射率是热红外遥感中最为关键的参数之一,在热红外遥感的定量化研究、地表能量平衡和地物填图等领域起着重要作用。但是,从热红外遥感数据反演地表发射率需要求解病态方程。提出一种基于神经网络模型逐像元获取地表发射率的方法。该方法基于MODIS可见光通道的反射率数据提取纹理特征,将纹理信息和可见光光谱信息进行融合作为神经网络的输入特征,发射率作为输出。获取的地表发射率结果和MODIS的标准发射率产品对比,平均误差为0.002。该方法直接建立地表发射率和地表反射率的关系,为单通道热红外卫星精确获取地表温度和发射率提供依据和可能。

Land surface emissivity is one of the most important parameters in thermal infrared remote sensing and plays a significant role in the quantitative study of thermal infrared remote sensing,the surface energy balance,and feature mapping.Retrieving the land surface emissivity from thermal infrared remote sensing data is a challenge because it presents an ill-posed problem.In this work,a method,which takes advantage of spectral and texture features of many visible channels available in the moderate resolution imaging spectroradiometer（MODIS）data and is based on backpropagation neural network to obtain land surface emissivity pixel-by-pixel,is presented.The method obtains the land surface emissivity map without the categorization of the land cover and the analysis indicates that the average error,compared to MODIS emissivity product,is within 0.002.It builds a direct relationship between reflectance data and emissivity data,and provides the possibility of obtaining precise emissivity data through the single channel thermal infrared satellite.