Assessment of the Stereo-Radargrammetric Mapping Potential of TerraSAR-X Multibeam Spotlight Data

Raggam, Hannes; Gutjahr, Karlheinz; Perko, Roland; Schardt, Mathias

doi:10.1109/tgrs.2009.2037315

Cited by 72 publications

(54 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CE90 values, i.e., the 90th percentile of the length of residual errors, are given in Table 1 (results taken from [14,15]). Our own studies conducted on TerraSAR-X data confirm those results [16,18]. Table 1.…”

Section: Test Sites and Datasupporting

confidence: 85%

“…The radargrammetric processing is described in detail in [18] and can be applied successfully due to the very exact pointing accuracy of the TerraSAR-X and COSMO-SkyMed sensor [14][15][16]. The main steps in the DSM extraction are pairwise stereo matching followed by a joint point intersection procedure.…”

Section: Multi-image Dsm Generationmentioning

confidence: 99%

“…Therefore, the pure stereo constellation 13 yield best results (cf. [18]). When analyzing the triplets, it can be seen that the triplet using cross-matching performs better.…”

Section: Multi-image Dsm Generationmentioning

confidence: 99%

“…The presented approach for digital surface model extraction for forest assessment is based on the authors' previous works [17][18][19] where 3D surface reconstruction was performed by stereo-radargrammetry. A core achievement is the multi-image matching concept for digital surface modeling that has been transferred from the authors' previous work based on optical satellite images [20] to radar data, incorporating the SAR specific image geometry.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Forest Assessment Using High Resolution SAR Data in X-Band

et al. 2011

View full text Add to dashboard Cite

Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric and interferometric data processing, as well as image segmentation and image classification. A core methodology is the multi-image matching concept for digital surface modeling based on geometrically constrained matching. Validation of generated surface models is made through comparison with LiDAR data, resulting in a standard deviation height error of less than 2 meters over forest. Image classification of forest regions is then based on X-band backscatter information, a canopy height model and interferometric coherence information yielding a classification accuracy above 90%. Such information is then directly used to extract forest border lines. High resolution X-band sensors deliver imagery that can be used for automatic forest assessment on a large scale.

show abstract

Section: Test Sites and Datasupporting

confidence: 85%

Section: Multi-image Dsm Generationmentioning

confidence: 99%

“…Therefore, the pure stereo constellation 13 yield best results (cf. [18]). When analyzing the triplets, it can be seen that the triplet using cross-matching performs better.…”

Section: Multi-image Dsm Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forest Assessment Using High Resolution SAR Data in X-Band

et al. 2011

View full text Add to dashboard Cite

show abstract

“…This geolocation accuracy was reported to be on the order of less than 1 m [13] and independent studies have later verified this [14,15], e.g., by using corner reflectors and the boundary condition that they are projected on known elevations. Raggam et al [16] could not fully validate this accuracy, but, nevertheless, concluded an overall accuracy of about 2 m. They also concluded that sensor model adjustments were sensitive with regards to Multi Looked Ground-Range Detected (MGD) products and that stereo mapping is a very challenging objective, in particular over forested or other terrain with high radiometric variability.…”

Section: Introductionmentioning

confidence: 99%

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Persson

Fransson

2014

Remote Sensing

View full text Add to dashboard Cite

Abstract:The last decade has seen launches of radar satellite missions operating in X-band with the sensors acquiring images with spatial resolutions on the order of 1 m. This study uses digital surface models (DSMs) extracted from stereo synthetic aperture radar images and a reference airborne laser scanning digital terrain model to calculate the above-ground biomass and tree height. The resulting values are compared to in situ data. Analyses were undertaken at the Swedish test sites Krycklan (64°N) and Remningstorp (58°N), which have different site conditions. The results showed that, for 459 forest stands in Remningstorp, biomass estimation at the stand level could be performed with 22.9% relative root mean square error, while the height estimation showed 9.4%. Many factors influenced the results and it was found that the topography has a significant effect on the generated DSMs and should therefore be taken into consideration when the stand level mean slope is above four degrees. Different tree species did not have a major effect on the models during leaf-on conditions. Moreover, correct estimation within young forest stands was problematic. The intersection angles resulting in the best results were in the range 8-16°. Based on the results in this study, radargrammetry appears to be a promising potential remote sensing technique for future forest applications.

show abstract