Segmentation and fusion of building features based on InSAR and optical data

A. Thiele; J. D. Wegner; E. Cadario; K. Schulz; U. Soergel

doi:10.1117/12.830373

7 October 2009 Segmentation and fusion of building features based on InSAR and optical data

A. Thiele, J. D. Wegner, E. Cadario, K. Schulz, U. Soergel

Proceedings Volume 7478, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX; 747803 (2009) https://doi.org/10.1117/12.830373
Event: SPIE Remote Sensing, 2009, Berlin, Germany

Abstract

The new generation of space borne SAR sensors provides geometric resolution of one meter, airborne systems even higher. In this high resolution data many features of urban objects become visible, which were beyond the scope of radar remote sensing only a few years ago. Focusing on elevated objects (e.g., urban area), layover, and occlusion issues inevitably arise because of the side-looking SAR sensor principle. In order to support interpretation, SAR data are often analyzed using additional information provided by maps or other remote sensing imagery. The focus of this paper is on building extraction in urban scenes by means of combined InSAR data and optical aerial imagery.

Citation Download Citation

A. Thiele, J. D. Wegner, E. Cadario, K. Schulz, and U. Soergel "Segmentation and fusion of building features based on InSAR and optical data", Proc. SPIE 7478, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX, 747803 (7 October 2009); https://doi.org/10.1117/12.830373

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available