Simultaneous Estimation of Land Surface and Atmospheric Parameters From Thermal Hyperspectral Data Using a LSTM-CNN Combined Deep Neural Network

发布时间： 2022-01-20 点击次数：

• 影响因子：0.0
• 发表刊物：IEEE Geoscience and Remote Sensing Letters
• 关键字：Deep neural network (DNN), hyperspectral remote sensing, simultaneous estimation, thermal airborne hyperspectral imager (TASI), thermal infrared (TIR)
• 摘要：Thermal infrared (TIR) remote sensing observation signal is influenced by both atmospheric and land surface conditions that are difficult to separate with conventional multichannel TIR data. Because of the advantage of channel wealth, hyperspectral TIR data can simultaneously estimate the land surface and atmospheric parameters using neural network models or integrating them with physical models. However, the commonly used neural network models do not fully explore the correlation between different channels by treating the input data as discrete features. Thus, this study aims to develop a new deep neural network (DNN) by combining the long short-term memory (LSTM) network and convolutional neural network (CNN) for estimating land surface temperature (LST), emissivity, atmospheric transmittance, upward radiance, and downward radiance more accurately. By applying on the thermal airborne
  hyperspectral imager (TASI) simulation dataset covering global atmospheric conditions with 32 channels in 8.0−11.5 µm, the pro
  posed model achieved results with the LST error of 0.95 K, the emissivity error of less than 0.012 for each channel, and the accuracy of three atmospheric parameters has also been improved compared with the current neural network models. Our model has been applied to a real TASI image, and its validity was further proved by the ground measurement validation data.
  Therefore, it can provide more reliable initial values for physical
  optimization models.
• 论文类型：期刊论文
• 学科门类：理学
• 一级学科：地理学
• 文献类型：J
• 卷号：19
• 期号：5508705
• 页面范围：1-5
• 是否译文：否
• 收录刊物：SCI
• 第一作者：Xin Ye
• 通讯作者：Huazhong Ren
• 全部作者：Jing Nie,Jian Hui,Jinshun Zhu,Chenchen Jiang,Wenjie Fan,Yonggang Qian,Yanzhen Liang
• 发表时间：2022-01-01