Abstract
Airborne hyperspectral data yield a new potential for spectrally-based identification, but also raise new challenges in image analysis caused by a high spatial and spectral variability of the urban environment. The algorithms have to analyze spectrally mixed and non-mixed-pixels of various classes which often show spectrally similar characteristics. In this context the authors developed a multi-technique approach which combines linear spectral unmixing and spectral classification for a complete inventory of main urban surface cover types. Despite the good results, problems remained in differentiation of spectrally similar surfaces, such as buildings and sealed open surfaces. The authors present an improved approach including a new algorithm for shape-based detection of buildings and new rules for an optimized pixel-oriented endmember selection. The approach was developed using DAIS hyperspectral image data of the reflective and thermal wavelength ranges covering a study area in the city of Dresden (Germany). In the result a much improved identification of urban surfaces was achieved due to the incorporation of shape-based techniques.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.