MULTI-TARGET DETECTION FROM FULL-WAVEFORM AIRBORNE LASER SCANNER USING PHD FILTER
We propose a new technique to detect multiple targets from full-waveform airborne laser scanner. We introduce probability hypothesis density (PHD) filter, a type of Bayesian filtering, by which we can estimate the number of targets and their positions simultaneously. PHD filter overcomes some limi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B5/647/2016/isprs-archives-XLI-B5-647-2016.pdf |
| Summary: | We propose a new technique to detect multiple targets from full-waveform airborne laser scanner. We introduce probability
hypothesis density (PHD) filter, a type of Bayesian filtering, by which we can estimate the number of targets and their positions
simultaneously. PHD filter overcomes some limitations of conventional Gaussian decomposition method; PHD filter doesn’t require
a priori knowledge on the number of targets, assumption of parametric form of the intensity distribution. In addition, it can take a
similarity between successive irradiations into account by modelling relative positions of the same targets spatially. Firstly we
explain PHD filter and particle filter implementation to it. Secondly we formulate the multi-target detection problem on PHD filter
by modelling components and parameters within it. At last we conducted the experiment on real data of forest and vegetation, and
confirmed its ability and accuracy. |
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| ISSN: | 1682-1750 2194-9034 |