光谱增强激光雷达在杂波中的异常检测
Anomaly Detection in Clutter using Spectrally Enhanced Ladar
摘要：
离散返回（DR）激光探测和测距（激光雷达）系统提供从场景中的物体反射的一系列回波。这些可以是第一个，最后一个或多个回声返回。相比之下，全波形（FW）激光雷达系统在一段时间内连续测量从物体反射的光的强度。例如，在一个虚拟场景中，为了探测隐藏在浓密树叶后面的物体，FW激光雷达穿透这些树叶并返回一系列回波，包括隐藏的微弱回波。本文的目的是学习当地的模式，其特点是测量光谱的同现回波。与这种模式的偏离定义了森林/树木深度剖面中的异常事件。据作者所知，DR或FW-Ladar以及几种光谱测量都没有应用于异常检测。这项工作提出了一种算法，允许检测的光谱和时间的异常在FW-多光谱激光雷达（FW-MSL）数据样本。异常被定义为不符合先前期望的全波形时间和频谱特征，其使用学习的子空间（字典）和使用重叠的时间窗口捕获共同出现的局部模式的系数集来表示。提出了一种改进的基于随机逼近的子空间学习优化方案。目标函数增加了一个判别项，表示子空间的可分性属性，并支持异常特征。该算法检测到几个人造物体和异常光谱隐藏在一个密集的植被杂波，也允许树种分类。
英文摘要：
Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a series of echoes that reflect from objects in a scene. These can be first, last or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure the intensity of light reflected from objects continuously over a period of time. In a camouflaged scenario, e.g., objects hidden behind dense foliage, a FW-Ladar penetrates such foliage and returns a sequence of echoes including buried faint echoes. The aim of this paper is to learn local-patterns of co-occurring echoes characterised by their measured spectra. A deviation from such patterns defines an abnormal event in a forest/tree depth profile. As far as the authors know, neither DR or FW-Ladar, along with several spectral measurements, has not been applied to anomaly detection. This work presents an algorithm that allows detection of spectral and temporal anomalies in FW-Multi Spectral Ladar (FW-MSL) data samples. An anomaly is defined as a full waveform temporal and spectral signature that does not conform to a prior expectation, represented using a learnt subspace (dictionary) and set of coefficients that capture co-occurring local-patterns using an overlapping temporal window. A modified optimization scheme is proposed for subspace learning based on stochastic approximations. The objective function is augmented with a discriminative term that represents the subspace's separability properties and supports anomaly characterisation. The algorithm detects several man-made objects and anomalous spectra hidden in a dense clutter of vegetation and also allows tree species classification.
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