{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:28:07Z","timestamp":1760243287860,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2014,6,10]],"date-time":"2014-06-10T00:00:00Z","timestamp":1402358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Geostationary meteorological satellite infrared (IR) channel data contain important spectral information for meteorological research and applications, but their spatial resolution is relatively low. The objective of this study is to obtain higher-resolution IR images. One common method of increasing resolution fuses the IR data with  high-resolution visible (VIS) channel data. However, most existing image fusion methods focus only on visual performance, and often fail to take into account the thermal physical properties of the IR images. As a result, spectral distortion occurs frequently. To tackle this problem, we propose a thermal physical properties-based correction method for fusing geostationary meteorological satellite IR and VIS images. In our two-step process, the high-resolution structural features of the VIS image are first extracted and incorporated into the IR image using regular multi-resolution fusion approach, such as the multiwavelet analysis. This step significantly increases the visual details in the IR image, but fake thermal information may be included. Next, the Stefan-Boltzmann Law is applied to correct the distortion, to retain or recover the thermal infrared nature of the fused image. The results of both the qualitative and quantitative evaluation demonstrate that the proposed physical correction method both improves the spatial resolution and preserves the infrared thermal properties.<\/jats:p>","DOI":"10.3390\/s140610187","type":"journal-article","created":{"date-parts":[[2014,6,10]],"date-time":"2014-06-10T11:50:51Z","timestamp":1402401051000},"page":"10187-10202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Thermal Physical Property-Based Fusion of Geostationary Meteorological Satellite Visible and Infrared Channel Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Lei","family":"Han","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, China"},{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences,  Beijing 100081, China"}]},{"given":"Lu","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, China"}]},{"given":"Yiling","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, China"}]},{"given":"Dalei","family":"Song","sequence":"additional","affiliation":[{"name":"College of Engineering, Ocean University of China, 238   Songling Road, Qingdao 266100, China"}]}],"member":"1968","published-online":{"date-parts":[[2014,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/19479830903561035","article-title":"Multi-source image sensing data fusion: Status and trends","volume":"1","author":"Zhang","year":"2010","journal-title":"Int. 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