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.
Highlights
Microwave sensors are able to penetrate through thick clouds to see the structure of a storm
By minimizing the Total variation (TV), we showed that the process significantly reduces the brightness temperature errors in the overall image
This paper develops a data fusion methodology and applies it to enhance the resolution of a microwave image using the data from a collocated infrared/visible sensor
Summary
Microwave sensors are able to penetrate through thick clouds to see the structure of a storm. The data (e.g., brightness temperatures) acquired by microwave (MW) sensors are blurry and of low-resolution, and all derived products, including rain rates will share that characteristic. The images obtained using infrared/visible sensors (IR/Vis), and their corresponding products can offer higher resolution but with negligible ability to penetrate into clouds. We can use the data from a collocated infrared/visible sensor to increase the resolution of a microwave image. By minimizing the TV, we showed that the process significantly reduces the brightness temperature errors in the overall image. These processes were rendered efficiently by employing methodologies based on current research in sparse optimization and compressed sensing. We performed the total variation-based deconvolution within the split Bregman optimization framework to achieve a significant computational time improvement over already robust total-variation gradient descent-based techniques
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