Thermal remote sensing over heterogeneous urban and suburban landscapes using sensor-driven super-resolution.

ABSTRACT: Thermal remote sensing is an important tool for monitoring regional climate and environment, including urban heat islands. However, it suffers from a relatively lower spatial resolution compared to optical remote sensing. To improve the spatial resolution, various "data-driven" image processing techniques (pan-sharpening, kernel-driven methods, and machine learning) have been developed in the previous decades. Such empirical super-resolution methods create visually appealing thermal images; however, they may sacrifice radiometric consistency because they are not necessarily sensitive to specific sensor features. In this paper, we evaluated a "sensor-driven" super-resolution approach that explicitly considers the sensor blurring process, to ensure radiometric consistency with the original t

SUBMITTER: Mizuochi H 

PROVIDER: S-EPMC8986004 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature