Name
Title | ||
|---|---|---|
Fusion of Multi-Baseline and Multi-Orbit InSAR DEMs with Terrain Feature-Guided Filter. |
Abstract | ||
|---|---|---|
Interferometric synthetic aperture radar (InSAR) is an effective technology for generating high-precision digital elevation models (DEMs). However, the vertical accuracy of InSAR DEMs is limited by the contradiction between height measurement sensitivity and phase unwrapping reliability in terms of normal baseline length as well as data voids caused by layover or shadow effects. In order to alleviate these two unfavorable factors, in this study, a novel InSAR DEM fusion method with guided filter is developed and assessed with multiple bistatic TanDEM-X InSAR data pairs of different normal baselines acquired from different orbits. Unlike the widely used fusion method based on pixel-by-pixel weighted average, the guided-filter-based method incorporates local spatial context information into the fusion and can thus effectively alleviate the noise effect and automatically fill in data voids. As a result of the local edge-preserving capability of the guided filter, the proposed fusion method can preserve terrain details by maintaining gradient consistency and introducing terrain features as guidance image. Furthermore, the proposed fusion method is computationally efficient owing to the linear time complexity of guided filter. The experimental results show that the fused DEM with guided filter can depict terrain details well and smooth the salt-and-pepper noise and fill in almost all of the data voids. The root mean square error (RMSE) of the fused InSAR DEM with guided filter is lower than those of the weighted average fused InSAR DEM and the TanDEM-X DEM released by the German Aerospace Center (DLR), thus validating the effectiveness of the fusion method proposed in this study. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/rs10101511 | REMOTE SENSING |
Keywords | Field | DocType |
InSAR DEM fusion,multi-baseline InSAR,layover and shadow,guided filter | Orbit,Computer vision,Interferometric synthetic aperture radar,Remote sensing,Terrain,Fusion,Artificial intelligence,Geology | Journal |
Volume | Issue | ISSN |
10 | 10 | 2072-4292 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuting Dong | 1 | 2 | 1.05 |
| Baobao Liu | 2 | 0 | 0.34 |
| Lu Zhang | 3 | 35 | 10.89 |
| Mingsheng Liao | 4 | 191 | 43.90 |
| Ji Zhao | 5 | 488 | 19.72 |