Enhanced resolution of microwave sounder imagery through fusion with infrared sensor data

Igor Yanovsky; Ali Behrangi; Yixin Wen; Mathias Schreier; Van Dang; Bjorn Lambrigtsen

doi:10.3390/rs9111097

Enhanced resolution of microwave sounder imagery through fusion with infrared sensor data

Igor Yanovsky, Ali Behrangi, Yixin Wen, Mathias Schreier, Van Dang, Bjorn Lambrigtsen

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

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.

Original language	English (US)
Article number	1097
Journal	Remote Sensing
Volume	9
Issue number	11
DOIs	https://doi.org/10.3390/rs9111097
State	Published - Nov 1 2017
Externally published	Yes

Keywords

Data fusion
Inverse problems
Precipitation
Remote sensing
Satellite imagery
Sparse optimization
Super-resolution

ASJC Scopus subject areas

General Earth and Planetary Sciences

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10.3390/rs9111097

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title = "Enhanced resolution of microwave sounder imagery through fusion with infrared sensor data",

abstract = "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.",

keywords = "Data fusion, Inverse problems, Precipitation, Remote sensing, Satellite imagery, Sparse optimization, Super-resolution",

author = "Igor Yanovsky and Ali Behrangi and Yixin Wen and Mathias Schreier and Van Dang and Bjorn Lambrigtsen",

note = "Funding Information: Acknowledgments: This work was supported by the National Aeronautics and Space Administration (NASA), Weather Research program. The work of I. Yanovsky was also supported by the National Science Foundation under Grant DMS 1217239. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Publisher Copyright: {\textcopyright} 2017 by the authors.",

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doi = "10.3390/rs9111097",

language = "English (US)",

volume = "9",

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T1 - Enhanced resolution of microwave sounder imagery through fusion with infrared sensor data

AU - Yanovsky, Igor

AU - Behrangi, Ali

AU - Wen, Yixin

AU - Schreier, Mathias

AU - Dang, Van

AU - Lambrigtsen, Bjorn

N1 - Funding Information: Acknowledgments: This work was supported by the National Aeronautics and Space Administration (NASA), Weather Research program. The work of I. Yanovsky was also supported by the National Science Foundation under Grant DMS 1217239. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Publisher Copyright: © 2017 by the authors.

PY - 2017/11/1

Y1 - 2017/11/1

N2 - 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.

AB - 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.

KW - Data fusion

KW - Inverse problems

KW - Precipitation

KW - Remote sensing

KW - Satellite imagery

KW - Sparse optimization

KW - Super-resolution

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Enhanced resolution of microwave sounder imagery through fusion with infrared sensor data

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