Multisensor Fusion of Landsat Images for High‐Resolution Thermal Infrared Images Using Sparse Representations

Abstract

Land surface temperature (LST) is an important parameter in the analysis of climate and human-environment interactions. Landsat Earth observation satellite data including a thermal band have been used for environmental research and applications; however, the spatial resolution of this thermal band is relatively low. This study investigates an efficient method of fusing Landsat panchromatic and thermal infrared images using a sparse representation (SR) technique. The application of SR is used for the estimation of missing details of the available thermal infrared (TIR) image to enhance its spatial features. First, we propose a method of building a proper dictionary considering the spatial resolution of the original thermal image. Second, a sparse representation relation between low- and high-resolution images is constructed in terms of the Landsat spectral response. We then compare the fused images created with different sampling factors and patch sizes. The results of both qualitative and quantitative evaluation show that the proposed method improves spatial resolution and preserves the thermal properties of basic LST data for use with environmental problems.

Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions

“…Regional and local processes have significant implications for household vulnerability [91], thus the study and management of socio-ecologic systems should be conducted with stakeholders in mind not only within the domains of HEI and HDGC. Emerging or underutilized methodologies and technologies such as thermal sensing [105,106], digital soil mapping [107], citizen science [72,108], UAVs, cloud computing, mobile mapping [108], or the use of "humans as sensors" [109,110] can guide informed decision-making in areas where data is hard to collect due to variability in socio-ecological settings [108] and will enhance the relevancy of future HDGC and HEI studies. The ability to run fast and complex computations based on long timeseries of remote sensing and environmental data in virtual or cloud-based environments such as Google Earth Engine or other National Aeronautical and Space Agency applications (Application for Extracting and Exploring Analysis Ready Samples-AppEEARS), for instance, will likely open up the specialized field of remote sensing to more social science applications and integrations.…”

Fusion of Landsat- 8 Thermal Infrared and Visible Bands With Multi-Resolution Analysis Contourlet Methods

“…It depends on the albedo, the vegetation cover and different types of land covers (Kumar et al, 2013). Thermal infrared bands are important data in climate research, weather forecast, hydrologic, ecological, urban climate, agricultural, geothermal and many other studies (Jin and Han, 2017).…”

“…Multi-resolution analysis is part of transform domain methods in pixel level image fusion, including discrete wavelet (DWT), curvelet (CVT), contourlet (CT), nonsubsampled contourlet (NSCT) and sharp frequency localization contourlet transforms (SFLCT) (Li et al, 2010). Jin and Han (2017) fused Landsat 7 panchromatic and thermal infrared images. They did this using sparse representation technique.…”

“…According to Landsat spectral response, a sparse representation relation between low and high resolution images was created. They concluded that their method improved spatial resolution and preserved the thermal properties of LST image (Jin and Han, 2017). Li et al (2010) compared curvelet and contourlet methods in image fusion.…”