Waveform decomposition is needed as a first step in the extraction of various types of geometric and spectral information from hyperspectral full-waveform LiDAR echoes. We present a new approach to deal with the ”Pseudo-monopulse” waveform formed by the overlapped waveforms from multi-targets when they are very close. We use one single skew-normal distribution (SND) model to fit waveforms of all spectral channels first and count the geometric center position distribution of the echoes to decide whether it contains multi-targets. The geometric center position distribution of the ”Pseudo-monopulse” presents aggregation and asymmetry with the change of wavelength, while such an asymmetric phenomenon cannot be found from the echoes of the single target. Both theoretical and experimental data verify the point. Based on such observation, we further propose a hyperspectral waveform decomposition method utilizing the SND mixture model with: 1) initializing new waveform component parameters and their ranges based on the distinction of the three characteristics (geometric center position, pulse width, and skew-coefficient) between the echo and fitted SND waveform and 2) conducting single-channel waveform decomposition for all channels and 3) setting thresholds to find outlier channels based on statistical parameters of all single-channel decomposition results (the standard deviation and the means of geometric center position) and 4) re-conducting single-channel waveform decomposition for these outlier channels. The proposed method significantly improves the range resolution from 60cm to 5cm at most for a 4ns width laser pulse and represents the state-of-the-art in ”Pseudo-monopulse” waveform decomposition.

The 101-channel full-waveform hyperspectral LiDAR (FWHSL) is able to simultaneously obtain geometric and spectral information of the target, and it is widely applied in 3D point cloud terrain generation and classification, vegetation detection, automatic driving, and other fields. Currently, most waveform data processing methods are mainly aimed at single or several wavelengths. Hidden components are revealed mainly through optimization algorithms and comparisons of neighbor distance in different wavelengths. The same target may be misjudged as different ones when dealing with 101 channels. However, using the gain decomposition method with dozens of wavelengths will change the spectral intensity and affect the classification. In this paper, for hundred-channel FWHSL data, we propose a method that can detect and re-decompose the channels with outliers by checking neighbor distances and selecting specific wavelengths to compose a characteristic spectrum by performing PCA and clustering on the decomposition results for object identification. The experimental results show that compared with the conventional single channel waveform decomposition method, the average accuracy is increased by 20.1%, the average relative error of adjacent target distance is reduced from 0.1253 to 0.0037, and the degree of distance dispersion is reduced by 95.36%. The extracted spectrum can effectively characterize and distinguish the target and contains commonly used wavelengths that make up the vegetation index (e.g., 670 nm, 784 nm, etc.).

In this study, the spatial structure of randomly distributed clusters of fungi An0429 spores was simulated using a cluster aggregation (CCA) model, and the single scattering parameters of fungi An0429 spores were calculated using the discrete dipole approximation (DDA) method. The transmittance of 10.6 µm infrared (IR) light in the aggregated fungi An0429 spores swarm is simulated by using the Monte Carlo method. Several parameters that affect the transmittance of 10.6 µm IR light, such as the number and radius of original fungi An0429 spores, porosity of aggregated fungi An0429 spores, and density of aggregated fungi An0429 spores of the formation aerosol area were discussed. Finally, the transmittances of microbial materials with different qualities were measured in the dynamic test platform. The simulation results showed that the parameters analyzed were closely connected with the extinction performance of fungi An0429 spores. By controlling the value of the influencing factors, the transmittance could be lower than a certain threshold to meet the requirement of attenuation in application. In addition, the experimental results showed that the Monte Carlo method could well reflect the attenuation law of IR light in fungi An0429 spore agglomerates swarms.

The k-nearest-neighbor simplex (kNNS) based on an adaptive C-mutual proportion standard deviation metric for classification of hyperspectral remote sensing imagery was proposed. By analyzing spectral characteristics on the samples of the same and different classes, a C-mutual proportion standard deviation metric is put forward which innovates a novel metric on distance and similarity measures for pattern recognition. Combined with the adaptive adjusting algorithm, this metric is used for the classification of hyperspectral remote sensing imagery. The traditional kNNS classification algorithm is improved by this metric and the adaptive adjusting algorithm, and its classification accuracy is enhanced. Three experiments with different types of hyperspectral imagery are conducted to evaluate the performance of the proposed algorithm in comparison to the other five classification algorithms. The experimental results demonstrate that the proposed algorithm is superior to other algorithms on overall accuracy and kappa coefficient.