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.).

The relationship between the properties of targets and the features of modulated waveforms is fundamental to remote sensing based on the full-waveform light detection and ranging (LiDAR). Developing a mathematical formula of modulated LiDAR waveforms is of great importance in establishing this relationship. In this study, we derive the mathematical formula of a laser echo waveform modulated by four typical targets: a rectangle, a square, a circle, and an equilateral triangle. By using these formulas, numerical calculations are performed to investigate the relationship between the properties of the targets and the features of the modulated waveform. The results show that, at a rotation angle of 80°, the modulated waveform changes from a Gaussian form to a non-Gaussian form and finally returns to a Gaussian form as the target size increases. When the target center deviates from the laser spot center and the rotation angle increases, the modulated waveform varies from Gaussian to non-Gaussian form, the value of the peak intensity decreases, and the position of the peak intensity shifts. The specific trends of these changes are successfully explained in terms of the geometric characteristics of the target and the spatial intensity distribution of the incident laser. The distinct dependencies of modulated waveforms on geometric shape, size, center position, and rotation angle indicate a convenient method for identity extraction and target recognition in remote sensing using full-waveform LiDAR. This offers exciting implications for applications, such as topological mapping, environment monitoring, and aerial target detection and recognition.

The relationship between the axial structures of three-dimensional (3-D) targets and the signal of the remote-sensing techniques, such as the modulated echo waveform of the full-waveform light detection and ranging (FW-LiDAR), has been well established. However, the relationship between the lateral structures on modulated echo waveform has not been exploited in detail. For FW-LiDAR, the peak intensity, rather than the shape and width, of the echo waveform reflects lateral structural information. Using four typical two-dimensional shapes to approach the lateral structures, a mathematical formula bridging the peak intensity and the lateral structures is derived. Based upon the echo waveform simulated using the formula, modulations of the lateral structural information on the peak intensity are examined to establish a database of target properties, including shape, size and position. The physics regarding peak intensity dependence on target position is used for facile shape recognition. The modulation of the lateral shapes of ground and aerial targets on the FW-LiDAR can be assessed extensively following the procedure demonstrated in this paper. Owing to the conciseness, the peak intensity formula enables the retrieval of lateral structural information by inversion, promoting the applications of FW-LiDAR in domains including topological mapping, ecological monitoring and space debris detection and removal.

Despite the tremendous endeavors devoted to exploiting the nature of glass transition, the factors that control the steepness index of viscosity near glass transition, that is, fragility, remain unclear. In this study, we demonstrate that, for polymeric and small molecular weight organic glass formers, fragility increases upward with increasing size of the free volume void at the glass transition temperature. This changing trend indicates that fragility is governed by the properties of the segments or molecular clusters in the free volume void rather than by the properties of the entire polymer chains. The physics behind the relationship between fragility and free volume void at the glass transition temperature is consistent with the physics behind the relationship between fragility and the molecular weight as well as the mechanics of the relationship between fragility and size of the cooperative units. This relationship also provides new insights into the understanding of the nature of the glass transition of polymeric and small molecular weight organic glass formers.