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“the Bridge To Resolution: Benefits Of Lawyers In Alternative Dispute Resolution” – A bridge neural network (BNN-) based framework can optimize the accuracy and enhance the applicability of remote sensing data interpretation. Credit: Space: Science & Technology Editors
In an example of the burgeoning field of ‘data fusion’, researchers have developed a neural network technique that bridges optical imaging and synthetic aperture radar into a single comprehensive data source. This approach combines various sets of information more capably than traditional methods.
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The approach is described in a paper in the Space: Science and Technology magazine published on October 12.
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Most contemporary techniques used to interpret information from remote sensing (such as from satellites or planes) are focused on single-modal data – that obtained from a single source of data collection. Such interpretive technologies rarely make full use of multiple sources (or ‘modes’), and so do not use complementary data that, when put together, can tell a fuller story of what is being observed.
One example of this is how optical imaging from satellites—the kind of passive beam scanning many scientists working with remote-sensing data will be familiar with—is rarely paired with synthetic aperture radar, or SAR. SAR is a form of radar that produces its own energy and then records the amount of energy that is reflected back after interacting with the Earth. Although optical imagery is similar to interpreting a photograph, SAR data requires a different way of thinking, as the signal is instead responsive to surface characteristics.
Crucially, unlike optical imaging, SAR is not defeated by challenging illumination conditions or clouds and fog. However, it suffers from a lot of data ‘noise’ and low texture details, which means that even well-trained experts sometimes struggle to interpret the result.
As a result, in the last decade or so, efforts to use artificial intelligence to combine multiple modes of data collection, such as both optical imaging and SAR, together into a single comprehensive source have begun to be developed. This fusing together of data from different modes or types of sensors is often called ‘data fusion’.
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This emerging area of innovation promises to revolutionize fields that use remote sensing – as varied as land-use monitoring, pollution prevention and military intelligence – by combining various sets of information in a single source faster, more comprehensively and more capably than traditional methods.
One critical area where data fusion promises to offer substantial benefit is in disaster response. Such activities would be more timely if SAR and optical imaging could be ‘data fused’ together as adverse weather and night would no longer be obstacles to rescue and monitoring work. In addition, trying to track down a missing flight like that of the infamous Malaysia Airlines Flight 370 that disappeared after leaving Kuala Lumpur in 2014 should become easier.
“At the time of the missing flight, human analysts struggled with the volume and variety of space-based data,” said Meiyu Huang, an assistant professor with the Qian Xuesen Laboratory of Space Technology. “If we can find a way to analyze SAR and optical imaging, rescuers should be able to use time-series remote sensing data from multiple sources to detect and pinpoint phenomena in very large areas much faster.”
The researchers developed a data fusion algorithm they call a bridge neural network, or BNN, to combine optical and SAR data. It enhances the observed features that are common to both data sources so as to assist the AI to better produce matches between them.
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First of all, the AI must be trained on a very large collection of SAR and optical data. So the researchers also put together a dataset of 20,000 pairs of perfectly aligned optical-SAR image matches from various high-resolution scenes of San Diego, Shanghai and Qingdao. The SAR images come from the Gaofen-3 satellite while the corresponding optical images are drawn from Google Earth. The collection, which they call the QXS-SAROPT, is made publicly available under an open access license for other researchers to use.
After training the BNN on the QXS-SAROPT dataset, the researchers tested the BNN model on an optical-to-SAR crossmodal object detection task on four benchmark detection datasets of ships at sea and in port. They found that their technique achieved up to a 96 percent accuracy rate.
This approach is far from limited to detecting boats or planes though. It can be applied to interpretive tasks such as segmenting and then checking the health of different crops, monitoring flood conditions and even identifying buildings under construction in rapidly urbanizing areas to assist with quantifying economic development in an area.
Going forward, the research group hopes to further develop a deep learning framework to integrate all possible steps (‘end to end’) in a data processing workflow on board. This should automate the process of wide-area search for detecting, monitoring and characterizing the progression of natural or human-caused events using time-series spectral images from several space-based sensors.
How Much Resolution Do You Really Need?
More information: Meiyu Huang et al, A Bridge Neural Network-Based Optical-SAR Image Joint Intelligent Interpretation Framework, Space: Science & Technology (2021). DOI: 10.34133/2021/9841456
Cite: Disaster relief could benefit from neural net combining multiple remote data sources (2021, December 21) Retrieved August 14, 2023 from https:///news/2021-12-disaster-relief-benefit-neural-net.html
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