Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms Using Gamma Markov Random Fields
Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms Using Gamma Markov Random Fields
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DOI：
10.1109/tci.2017.2703144
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发表时间：
2017-12-01
影响因子：
5.4
通讯作者：
Hero, Alfred
中科院分区：
文献类型：
--
作者：
Altmann, Yoann;Maccarone, Aurora;Hero, Alfred
提出了一种新的贝叶斯光谱分解算法，用于分析稀疏多光谱激光雷达遥感图像。对于第一个近似值，在目标存在的情况下，每个激光雷达波形由一个主峰组成，主峰的位置取决于目标距离，其幅度取决于所考虑的激光光源的波长(即，目标反射率)。此外，这些时间响应通常被假设为在低光子计数区被泊松噪声破坏。当考虑多个波长时，除了基于激光雷达的距离轮廓估计之外，还可以使用光谱信息来识别和量化场景中的主要材料。由于其异常检测能力，所提出的分层贝叶斯模型，结合高效的马尔可夫链蒙特卡罗算法，允许稳健地估计深度图像以及与所观测的三维场景相关联的丰富图和离群图。通过在受控环境中获取的真实多光谱激光雷达数据进行的实验，说明了所提出的方法。结果表明，混合由极稀疏的光子计数(每个像素和波段少于10个光子)构成的光谱响应是可能的。
This paper presents a new Bayesian spectral unmixing algorithm to analyze remote scenes sensed via sparse multispectral Lidar measurements. To a first approximation, in the presence of a target, each Lidar waveform consists of a main peak, whose position depends on the target distance and whose amplitude depends on the wavelength of the laser source considered (i.e., on the target reflectivity). Besides, these temporal responses are usually assumed to be corrupted by Poisson noise in the low photon count regime. When considering multiple wavelengths, it becomes possible to use spectral information in order to identify and quantify the main materials in the scene, in addition to estimation of the Lidar-based range profiles. Due to its anomaly detection capability, the proposed hierarchical Bayesian model, coupled with an efficient Markov chain Monte Carlo algorithm, allows robust estimation of depth images together with abundance and outlier maps associated with the observed three-dimensional scene. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data acquired in a controlled environment. The results demonstrate the possibility to unmix spectral responses constructed from extremely sparse photon counts (less than 10 photons per pixel and band).