Residual tensor analysis for quality assessment of data integration

Rey-Jer You, B. C. Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fusion of different data sets in the geospatial field is often used in order to complement the advantages and disadvantages of individual data sets, such as integration of image data and vector data, fusion of Photogrammetric data and LiDAR data, or combination of cadastral data and image data etc. An important step in the data fusion is to register these data sets to a common coordinate system. In this paper, we use residuals after registration to develop a novel quality assessment method, namely residual tensor analysis, for fusion of LiDAR and topographic map data. The results show that our residual tensor method is superior to the common fitting error analysis. In particular, our residual tensor is very useful as a quality indicator for automatic building model construction using LiDAR and topographic map data.

Original languageEnglish
Title of host publication33rd Asian Conference on Remote Sensing 2012, ACRS 2012
Pages1934-1938
Number of pages5
Volume3
Publication statusPublished - 2012
Event33rd Asian Conference on Remote Sensing 2012, ACRS 2012 - Pattaya, Thailand
Duration: 2012 Nov 262012 Nov 30

Other

Other33rd Asian Conference on Remote Sensing 2012, ACRS 2012
CountryThailand
CityPattaya
Period12-11-2612-11-30

All Science Journal Classification (ASJC) codes

  • Information Systems

Fingerprint Dive into the research topics of 'Residual tensor analysis for quality assessment of data integration'. Together they form a unique fingerprint.

  • Cite this

    You, R-J., & Lin, B. C. (2012). Residual tensor analysis for quality assessment of data integration. In 33rd Asian Conference on Remote Sensing 2012, ACRS 2012 (Vol. 3, pp. 1934-1938)