基于深度卷积网络的时空温度融合

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL Photogrammetric Engineering and Remote Sensing Pub Date : 2022-02-01 DOI:10.14358/pers.21-00023r2
Xuehan Wang, Z. Shao, Xiao Huang, D. Li
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引用次数: 1

摘要

高时空分辨率地表温度(LST)图像在各个研究领域都是必不可少的。然而,由于技术限制,传感系统难以同时提供高空间和高时间分辨率的地表温度。在这项研究中,我们提出了一种多尺度时空温度图像融合网络(MSTTIFN)来生成高空间分辨率的地表温度产品。MSTTIFN在输入的MODIS (Moderate Resolution Imaging Spectroradiometer, MODIS) lst和输出的Landsat lst之间建立了两对参考数据的非线性映射,从而提高了时间序列lst的分辨率。我们在两个研究区域(北京和山东)对Landsat和MODIS的实际数据进行了实验,并将我们提出的MSTTIFN与四种竞争方法进行了比较:时空自适应反射融合模型、灵活时空数据融合模型、两流卷积神经网络(StfNet)和基于深度学习的时空温度融合网络。结果表明,MSTTIFN具有最优、最稳定的性能。
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Spatiotemporal Temperature Fusion Based on a Deep Convolutional Network
High-spatiotemporal-resolution land surface temperature (LST) images are essential in various fields of study. However, due to technical constraints, sensing systems have difficulty in providing LSTs with both high spatial and high temporal resolution. In this study, we propose a multi-scale spatiotemporal temperature-image fusion network (MSTTIFN) to generate high-spatial-resolution LST products. The MSTTIFN builds nonlinear mappings between the input Moderate Resolution Imaging Spectroradiometer (MODIS) LSTs and the out- put Landsat LSTs at the target date with two pairs of references and therefore enhances the resolution of time-series LSTs. We conduct experiments on the actual Landsat and MODIS data in two study areas (Beijing and Shandong) and compare our proposed MSTTIFN with four competing methods: the Spatial and Temporal Adaptive Reflectance Fusion Model, the Flexible Spatiotemporal Data Fusion Model, a two-stream convolutional neural network (StfNet), and a deep learning-based spatiotemporal temperature-fusion network. Results reveal that the MSTTIFN achieves the best and most stable performance.
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来源期刊
Photogrammetric Engineering and Remote Sensing
Photogrammetric Engineering and Remote Sensing 地学-成像科学与照相技术
CiteScore
1.70
自引率
15.40%
发文量
89
审稿时长
9 months
期刊介绍: Photogrammetric Engineering & Remote Sensing commonly referred to as PE&RS, is the official journal of imaging and geospatial information science and technology. Included in the journal on a regular basis are highlight articles such as the popular columns “Grids & Datums” and “Mapping Matters” and peer reviewed technical papers. We publish thousands of documents, reports, codes, and informational articles in and about the industries relating to Geospatial Sciences, Remote Sensing, Photogrammetry and other imaging sciences.
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