Enhanced Resolution of Microwave Sounder Imagery through Fusion with Infrared Sensor Data

The images acquired by microwave sensors are blurry and have low resolution. On the other hand, the images obtained using infrared/visible sensors are often of higher resolution. In this paper, we develop a data fusion methodology and apply it to enhance the resolution of a microwave image using the...

Full description

Saved in:
Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 9; no. 11; p. 1097
Main Authors Yanovsky, Igor, Behrangi, Ali, Wen, Yixin, Schreier, Mathias, Dang, Van, Lambrigtsen, Bjorn
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2017
Subjects
Data acquisition
data fusion
Data integration
Image acquisition
Image enhancement
Infrared detectors
Infrared imagery
Infrared instruments
Infrared radiometers
inverse problems
Microwave sensors
Microwave sounding
Model accuracy
Multisensor fusion
precipitation
remote sensing
satellite imagery
Sensors
sparse optimization
Spatial discrimination
Spatial resolution
super-resolution
Online AccessGet full text

Cover

More Information
Summary:The images acquired by microwave sensors are blurry and have low resolution. On the other hand, the images obtained using infrared/visible sensors are often of higher resolution. In this paper, we develop a data fusion methodology and apply it to enhance the resolution of a microwave image using the data from a collocated infrared/visible sensor. Such an approach takes advantage of the spatial resolution of the infrared instrument and the sensing accuracy of the microwave instrument. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. We tested our method using a precipitation scene captured with the Advanced Microwave Sounding Unit (AMSU-B) microwave instrument and the Advanced Very High Resolution Radiometer (AVHRR) infrared instrument and compared the results to simultaneous radar observations. We show that the data fusion product is better than the original AMSU-B and AVHRR observations across all statistical indicators.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs9111097