Michael Recla
Papers
1
Total Citations
46
H-Index
1
About
Michael Recla is a leading researcher at the intersection of remote sensing and deep learning, with a primary focus on advancing single-image height reconstruction from synthetic aperture radar (SAR) data. His most influential work, the 2021 paper “Deep-learning-based single-image height reconstruction from very-high-resolution SAR intensity data,” has garnered 46 citations and stands as a cornerstone in the field. Recla pioneered the adaptation of deep learning-based single-image depth estimation (SIDE)—originally developed for robotics and autonomous driving—to the unique challenges of remote sensing. By demonstrating that height maps can be reliably estimated from a single SAR intensity image, he opened new possibilities for 3D terrain modeling without the need for stereo or multi-pass interferometry. This breakthrough has significant implications for disaster monitoring, urban planning, and defense surveillance. Recla’s work bridges computer vision and geoscience, showing how techniques from one domain can revolutionize another. His contributions are widely recognized for their practical impact, enabling faster, more cost-effective height estimation from spaceborne sensors.
Research Focus
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Top Papers
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