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A Multi-Source InSAR DEM Reconstruction Framework Based on a Complexity Factor
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DOI：10.1109/TGRS.2024.3523435.png)
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Abstract
En 中文
The digital elevation model (DEM) reconstruction accuracy of single-channel interferometric synthetic aperture radar (SC-InSAR) is limited by the SAR side-looking imaging geometry, decorrelations, phase unwrapping (PU), and so on. With the availability of increasing InSAR data, to overcome the limitations of SC-InSAR, a multi-source InSAR DEM reconstruction framework based on a complexity factor is proposed in this article. To simultaneously take the effects of noise level and terrain slope into account, a complexity factor for each interferometric pair is constructed. Next, to reduce the PU failure rate for each pair, this factor is used to guide the two-stage programming approach (TSPA) PU method. Then, to avoid the adverse effects of PU failure on elevation fusion, unreliable pixels of each pair are detected by exploiting the complexity factor. Finally, after multiple elevations from different side-looking directions are obtained, the elevation-weighted fusion is performed to reconstruct the final DEM in the map projection coordinate system. Experimental results on real multi-source InSAR data demonstrate that the complexity factor can effectively guide the steps of TSPA PU, detection of unreliable pixels, and elevation-weighted fusion in the proposed framework, thereby improving the DEM reconstruction accuracy for mountainous areas with complex and steep terrain.
Keywords:
Image reconstruction
Complexity theory
Accuracy
Synthetic aperture radar
Coherence
Maximum likelihood estimation
Orbits
Optical interferometry
Noise level
Electronic mail
Complexity factor
digital elevation model (DEM) reconstruction
interferometric synthetic aperture radar (InSAR)
multi-source InSAR
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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