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

999 International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 83 STRATEGIES AND METHODS FOR THE FUSION OF DIGITAL ELEVATION MODELS FROM OPTICAL AND SAR DATA M. Honikel Institute of Geodesy and Photogrammetry, ETH Zürich-Hönggerberg, CH-8093 Zürich, marc.honikel@geod.ethz.ch KEYWORDS: DEM, ERS, SPOT, Data fusion. ABSTRACT Generation of digital elevation models (DEMs) is a main issue of remote sensing, as precise DEMs are needed in a wide range of remote sensing applications. The most widespread techniques for DEM generation are stereoscopy for optical sensor images and interferometry for SAR images. Both techniques suffer from certain sensor and processing limitations, which can be overcome by the synergetic use of both sensors and DEMs respectively. In this paper, different strategies for fusing SAR and optical data are combined to derive high quality DEM products. Quality in this context means accuracy of heights. An estimation of the error properties of both datasets is derived both from the data processing and the single DEMs themselves. The stereo-optical and In SAR techniques have distinct, partially complementary, error properties. Two techniques, which take advantage of the complementary properties of InSAR and stereo-optical DEMs, will be applied for the fusion process. First, as cross-correlation plays a major role for the height measurement in both techniques, the cross-correlation of phases, respectively grey values, are determined for each DEM point and used as weight for the DEM fusion. In the second technique to be applied, by taking advantage of the fact that errors of the DEMs are of different nature, affected parts are filtered and replaced by those of the counterpart. The results of both techniques will be fused in a final step. This procedure is tested with two sets of SPOT and ERS DEMs of regions in south-central Catalonia, resulting in a remarkable improvement in DEM accuracy and representation of the terrain. 1. INTRODUCTION The two main techniques for DEM generation from satellite imagery are stereoscopy with optical sensors and interferometry with SAR sensors. The height is measured in different ways in both techniques. InSAR computes the height from the phase difference of the point backscatter in two passes. Since phases in an interferogram are wrapped in an 2n interval, the critical step in the generation of elevation models with SAR interferometry is phase unwrapping, i.e. the solution of these phase ambiguities. Phase unwrapping becomes extremely difficult in cases of low signal to noise ratio due to decorrelation of the phase measurements. In the optical case, the coordinate difference of conjugate points in both images must be measured for the height determination. Therefore, the correct identification (matching) of conjugate points is essential for accurate stereo height measurements. Again, decorrelation handicaps the matching or even leads to point mismatching. As both techniques are based on different principles and sensors, the question arises, how these independent height measurements can be fused to obtain DEMs, which do not suffer from the single sensor limitations and consist only of valid measurements of each single source. Obviously, this task goes beyond the straightforward (yet not simple) identification and exchange of data in regions, where the height measurement of one of the contributing sensors fails (e.g. occlusions, layover), as it would not take advantage of the independent measurements as a whole. An estimation of the height errors is needed for any fusion process, in order to profit from both sensors. Provided a measure is found, which is directly related to the height error, it can be used to define a weight according to which the DEMs can be fused. As correlation is a critical factor for both techniques, the correlation coefficient of both measurements will be introduced as weight for the proposed fusion method. Although the correlation coefficient has different meaning in both techniques, it still relates to the measurement error. 2. INSAR AND STEREO-OPTICAL DEM ERRORS Like all measurements, the measurement of topographic heights is error corrupted for various reasons. According to the error model introduced by Baarda (1967), three general types of errors have to be distinguished: • systematic errors, which occur in all measurements due to measurement inconsistencies. • random errors, which are inevitable and nonpredictable. They can be described as random variables. • blunders, which are large and occur due to an erroneous handling of the measurement process. This error model, originally designed for geodetic networks, applies to redundant measurements of the same parameter. As we deal here with single measurements with different sensors, the model must be modified, as an assumption of redundancy would be invalid in this case. The systematic error in this context applies to a global error, which affects all measurements within a DEM (e.g. baseline

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