Abstract
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.
Highlights
Full-waveform airborne laser scanner becomes widely used in LiDAR
Reflection intensity data acquired by full-waveform airborne laser scanner is expected to be useful compared with traditional discrete-return laser system, especially in the case multiple target exist above ground
Taking multi-target detection as an example, under a set of timeseries observations Z1:k = {Z1, Z2,..., Zk} from sensors, we can estimate the number of targets n(k) and their positon xk at time k: Xk ={x1, x2,...,xn(k)} by probability hypothesis density (PHD) filter
Summary
Full-waveform airborne laser scanner becomes widely used in LiDAR. Reflection intensity data acquired by full-waveform airborne laser scanner is expected to be useful compared with traditional discrete-return laser system, especially in the case multiple target exist above ground. Our idea is that the problem of variable-number multi-target tracking by still sensor is similar to the problem of variablenumber multi-target detection by moving sensor, e.g. aerial laser scanner. The structure of these problems are almost the same; what we can observe are generated stochastically by latent variables, or “state”, and state follows a certain dynamics and it sometimes appears and disappears. We explain PHD filter, formulate multi-target detection problem by using it, and apply a proposed method to the acquired data of forest and vegetation
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