光学学报, 2017, 37 (11): 1128001, 网络出版: 2018-09-07
基于深度残差网络的高光谱遥感数据霾监测 下载: 1238次
Hyperspectral Data Haze Monitoring Based on Deep Residual Network
遥感 大气污染监测 霾监测 深度残差网络 高光谱遥感 深度学习 机器学习 remote sensing air pollution monitoring haze monitoring deep residual network hyperspectral remote sensing deep learning machine learning
摘要
霾监测是环境治理中的关键技术之一。目前地面观测站进行霾监测的耗费较大,基于多光谱遥感的霾识别精度较低。将深度学习用于高光谱遥感数据的霾监测,提出一种基于深度残差网络的高光谱霾监测方法,利用深度网络提取霾光谱曲线特征,再使用残差学习等方法降低网络训练难度,得到了霾监测模型。苏州地区Hyperion高光谱数据集上的实验表明,与其他遥感霾监测方法相比,所提方法的霾识别精度更高。
Abstract
Haze monitoring is one of the key technologies for environmental governance. At present, the cost of the ground haze monitoring is very high and the accuracy of the multispectral remote sensing haze monitoring is low. The hyperspectral sensing data haze monitoring is studied by deep learning. A hyperspectral haze monitoring algorithm based on deep residual network is presented. The features of haze hyperspectral curves are obtained with the deep network. The difficulty of the network training is decreased with the residual leaning method, and a haze monitoring model is achieved. The experimental results of the Suzhou Hyperion hyperspectral data sets show that, compared with other methods of remote haze monitoring, the proposed method has higher recognition accuracy in haze monitoring.
陆永帅, 李元祥, 刘波, 刘辉, 崔林丽. 基于深度残差网络的高光谱遥感数据霾监测[J]. 光学学报, 2017, 37(11): 1128001. Yongshuai Lu, Yuanxiang Li, Bo Liu, Hui Liu, Linli Cui. Hyperspectral Data Haze Monitoring Based on Deep Residual Network[J]. Acta Optica Sinica, 2017, 37(11): 1128001.