Title
Multispectral Radiometric Analysis of Façades to Detect Pathologies from Active and Passive Remote Sensing.
Abstract
This paper presents a radiometric study to recognize pathologies in facades of historical buildings by using two different remote sensing technologies covering part of the visible and very near infrared spectrum (530-905 nm). Building materials deteriorate over the years due to different extrinsic and intrinsic agents, so assessing these affections in a non-invasive way is crucial to help preserve them since in many cases they are valuable and some have been declared monuments of cultural interest. For the investigation, passive and active remote acquisition systems were applied operating at different wavelengths. A 6-band Mini-MCA multispectral camera (530-801 nm) and a FARO Focus3D terrestrial laser scanner (905 nm) were used with the dual purpose of detecting different materials and damages on building facades as well as determining which acquisition system and spectral range is more suitable for this kind of studies. The laser scan points were used as base to create orthoimages, the input of the two different classification processes performed. The set of all orthoimages from both sensors was classified under supervision. Furthermore, orthoimages from each individual sensor were automatically classified to compare results from each sensor with the reference supervised classification. Higher overall accuracy with the FARO Focus3D, 74.39%, was obtained with respect to the Mini MCA6, 66.04%. Finally, after applying the radiometric calibration, a minimum improvement of 24% in the image classification results was obtained in terms of overall accuracy.
Year
DOI
Venue
2016
10.3390/rs8010080
REMOTE SENSING
Keywords
Field
DocType
cultural heritage,multispectral camera,laser scanning,radiometric calibration,remote sensing,close range photogrammetry,multispectral classification
Radiometric calibration,Close range photogrammetry,Computer vision,Laser scanning,Remote sensing,Radiometric analysis,Multispectral image,Multispectral pattern recognition,Artificial intelligence,Geology,Contextual image classification
Journal
Volume
Issue
ISSN
8
1
2072-4292
Citations 
PageRank 
References 
0
0.34
0
Authors
6