Abstract
| Original language | English |
|---|---|
| Title of host publication | 2016 24th European Signal Processing Conference (EUSIPCO) |
| Publisher | IEEE |
| Pages | 513-517 |
| Number of pages | 5 |
| ISBN (Electronic) | 9780992862657 |
| DOIs | |
| Publication status | Published - 1 Dec 2016 |
| Event | 24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary Duration: 29 Aug 2016 → 2 Sep 2016 Conference number: 24 |
Publication series
| Name | European Signal Processing Conference (EUSIPCO) |
|---|---|
| Publisher | IEEE |
| ISSN (Print) | 2076-1465 |
Conference
| Conference | 24th European Signal Processing Conference 2016 |
|---|---|
| Abbreviated title | EUSIPCO 2016 |
| Country | Hungary |
| City | Budapest |
| Period | 29/08/16 → 2/09/16 |
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Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms. / Altmann, Yoann; Maccarone, Aurora; McCarthy, Aongus; Buller, Gerald Stuart; McLaughlin, Stephen.
2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. p. 513-517 (European Signal Processing Conference (EUSIPCO)).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral lidar waveforms
AU - Altmann, Yoann
AU - Maccarone, Aurora
AU - McCarthy, Aongus
AU - Buller, Gerald Stuart
AU - McLaughlin, Stephen
PY - 2016/12/1
Y1 - 2016/12/1
N2 - This paper presents a new Bayesian clustering method to analyse remotescenes sensed via multispectral Lidar measurements. To a firstapproximation, each Lidar waveform mainly consists of the temporalsignature of the observed target, which depends on the wavelengthof the laser source considered and which is corrupted by Poissonnoise. By sensing the scene at several wavelengths, we expect amore accurate target range estimation and a more efficient spectralanalysis of the scene. Thanks to its spectral classification capability,the proposed hierarchical Bayesian model, coupled with an efficientMarkov chain Monte Carlo algorithm, allows the estimation of depthimages together with reflectivity-based scene segmentation images.The proposed methodology is illustrated via experiments conductedwith real multispectral Lidar data.
AB - This paper presents a new Bayesian clustering method to analyse remotescenes sensed via multispectral Lidar measurements. To a firstapproximation, each Lidar waveform mainly consists of the temporalsignature of the observed target, which depends on the wavelengthof the laser source considered and which is corrupted by Poissonnoise. By sensing the scene at several wavelengths, we expect amore accurate target range estimation and a more efficient spectralanalysis of the scene. Thanks to its spectral classification capability,the proposed hierarchical Bayesian model, coupled with an efficientMarkov chain Monte Carlo algorithm, allows the estimation of depthimages together with reflectivity-based scene segmentation images.The proposed methodology is illustrated via experiments conductedwith real multispectral Lidar data.
U2 - 10.1109/EUSIPCO.2016.7760301
DO - 10.1109/EUSIPCO.2016.7760301
M3 - Conference contribution
T3 - European Signal Processing Conference (EUSIPCO)
SP - 513
EP - 517
BT - 2016 24th European Signal Processing Conference (EUSIPCO)
PB - IEEE
ER -