Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 4 FUSION OF SENSOR-DERIVED PRODUCTS

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: STRATEGIES AND METHODS FOR THE FUSION OF DIGITAL ELEVATION MODELS FROM OPTICAL AND SAR DATA. M. Honikel

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 84 errors). Height measurement fails for various reasons all over the DEM (e.g. temporal (radiometric or geometric) changes or sensor noise), therefore all resulting random errors will be summarized in a statistic error. The definition of blunders is adopted from the common multi-measurement case (e.g. wrong determination of ground control points). Note that blunders and systematic errors have (statistically) the same effect, as they cause an offset in the expectation values of the observations. 3. CORRELATION COEFFICIENT The correlation coefficient R is defined as [x\xi] a/ £ 1 [xix\] \e\ [X2X2) (-1 <p< 1) (1) Several authors give surveys of InSAR and stereo-optical DEM errors. Grim (1986), who refers to the Baarda error model, gives a detailed error description for photogrammetric sensor modelling. Zebker et al. (1994) gave an evaluation of ERS topographic map accuracy, dividing errors according to their effects in statistic and systematic errors. Each error type will occur in both InSAR and stereo-optical DEMs. In contrast to systematic errors and blunders, statistic errors of phase and parallax measurements occur locally, but may turn affected regions unusable. Typical reasons for statistic errors in spacebome DEM generation are temporal changes between both passes. As the InSAR procedure is very sensitive to a change of backscatter geometry, the errors in InSAR DEMs increase with the repeat pass time interval. Additional problems with the handling of mountainous terrain lead, in comparison to optical sensors, to a generally less robust performance. The presentation of erroneous heights is different in both DEMs. The stereo-optical DEM is affected by spikes, which make the DEM appear rugged. These spikes originate from the mismatching of conjugate points in the stereo pair. The resulting parallax causes a wrong height estimation, i.e. the point lies above or below the terrain (Fig. 4). The localization of these errors seems to be much easier than the error detection in the InSAR DEM. Although residues in the interferogram appear as phase jumps higher than 7t, these values are either not used or replaced by surrounding phase estimates, depending on the phase unwrapping algorithm. For this study, the least squares (LS) unwrapping algorithm (Ghiglia and Romero, 1994) has been applied, which estimates heights by integrating the phase vector gradient field under a smooth solution constraint, thus suppressing residuals and yielding a global solution. Although this algorithm performs robustly in comparison to the tree algorithm (Goldstein et al., 1988), errors are still localized in areas of low correlation in the LS DEM (Zebker and Lu, 1998). As stated above, interferometric and stereo DEM generation requires the correct identification of the same point in both images, in order to measure the phase difference or the parallax. Unlike systematic errors, which can be reduced by suitable choice of parameters, the reduction of statistic errors is the challenging act for the fusion process. The next section shows the meaning of the correlation coefficient for both techniques. It will be used as an indicator of the presence and extent of noise and consequently of the statistic error of each measurement. where E{Xi} denotes the expectation value of the statistic variable Xi, hence E{(Xi) 2 } is the variance and E{X1X2} the covariance of XI and X2. Note that coherence denotes the absolute value of p in interferometry. The correlation coefficient between two images is used in both techniques, but has for each technique a different meaning. In the stereo-optical case, points are matched by maximising the correlation coefficient within the correlation window. Therefore, the correlation coefficient of each DEM point shows the maximum correlation value occurring during the matching process. Therefore, low correlation indicates not necessarily a point error, but points, where matching problems may have occurred. Low correlation of grey values results from radiometric differences between the images, due to multitemporal and across track data acquisition (case of SPOT stereo viewing), sensor noise or atmospheric effects due to scattering and absorption, etc. Therefore, a low correlation coefficient will occur randomly all over a DEM, wherever differences between the stereo images exist (Fig. 3). Image pre-processing, like filtering and introduction of geometric and radiometric constraints are highly needed in the multitemporal case in order to reduce the effects of radiometric differences on the matching algorithm (Baltsavias and Stallmann, 1993). In the InSAR case, a high coherence indicates those areas, where phase estimation for DEM generation is reasonable. Even more, the cross-correlation would give an estimate of the height error, if it was known precisely, as a = K (2) with P 1-/7 (3) a: height error K: constant term E: signal to noise ratio Note that only a correlation estimate is computed within an estimator window from the flattened interferogram (Small et al., 1995). Therefore, the estimated height error is dependent on the size of the estimator window (Prati et al., 1994). Still, the correlation coefficient can be used as a confidence map of the InSAR DEM.

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