Uncertainty in multispectral lidar signals caused by incidence angle effects

Spectral missing color correction based on an adaptive parameter fitting model

With the development of remote sensing technology, true-color visualization of hyperspectral LiDAR echo signals has become a hotspot for both academic research and commercial applications. The limitation of the emission power of hyperspectral LiDAR causes the loss of spectral-reflectance information in some channels of the hyperspectral LiDAR echo signal. The color reconstructed based on the hyperspectral LiDAR echo signal is bound to have serious color cast problem. To solve the existing problem, a spectral missing color correction approach based on adaptive parameter fitting model is proposed in this study. Given the known missing spectral-reflectance band intervals, the colors in incomplete spectral integration are corrected to accurately restore target colors. Based on the experimental results, the color difference between color blocks and the hyperspectral image corrected by the proposed color correction model is smaller than that of the ground truth, and the image quality is higher, realizing the accurate reproduction of the target color.

True color 3D imaging optimization with missing spectral bands based on hyperspectral LiDAR

True color 3D imaging plays an essential role in expressing target characteristics and 3D scene reconstruction. It can express the colors, and spatial position of targets and is beneficial for classification and identification to investigate the target material. As a special case of target imaging, true color 3D imaging is important in understanding and reconstructing real scenes. The fusion of 3D point clouds with RGB images can achieve object reconstructions, yet varying illumination conditions and registration problems still exist. As a new active imaging technique, hyperspectral LiDAR (HSL) system, can avoid these problems through hardware configuration, and provide technical support for reconstructing 3D scenes. The spectral range of the HSL system is 431-751nm. However, spectral information obtained with HSL measurements may be influenced by various factors, that further impinge on the true color 3D imaging. This study aims to propose a new color reconstruction method to improve color reconstruction challenges with missing spectral bands. Two indoor experiments and five color reconstruction schemes were utilized to evaluate the feasibility and repeatability of the method. Compared with the traditional method of color reconstruction, color reconstruction effect and color similarity were considerably improved. The similarity of color components was improved from 0.324 to 0.762. Imaging results demonstrated the reliability of improving color reconstruction effect with missing spectral bands through the new method, thereby expanded the application scopes of HSL measurements.

Analysis and Radiometric Calibration for Backscatter Intensity of Hyperspectral LiDAR Caused by Incident Angle Effect

Hyperspectral LiDAR (HSL) is a new remote sensing detection method with high spatial and spectral information detection ability. In the process of laser scanning, the laser echo intensity is affected by many factors. Therefore, it is necessary to calibrate the backscatter intensity data of HSL. Laser incidence angle is one of the important factors that affect the backscatter intensity of the target. This paper studied the radiometric calibration method of incidence angle effect for HSL. The reflectance of natural surfaces can be simulated as a combination of specular reflection and diffuse reflection. The linear combination of the Lambertian model and Beckmann model provides a comprehensive theory that can be applied to various surface conditions, from glossy to rough surfaces. Therefore, an adaptive threshold radiometric calibration method (Lambertian–Beckmann model) is proposed to solve the problem caused by the incident angle effect. The relationship between backscatter intensity and incident angle of HSL is studied by combining theory with experiments, and the model successfully quantifies the difference between diffuse and specular reflectance coefficients. Compared with the Lambertian model, the proposed model has higher calibration accuracy, and the average improvement rate to the samples in this study was 22.67%. Compared with the results before calibration with the incidence angle of less than 70°, the average improvement rate of the Lambertian–Beckmann model was 62.26%. Moreover, we also found that the green leaves have an obvious specular reflection effect near 650–720 nm, which might be related to the inner microstructure of chlorophyll. The Lambertian–Beckmann model was more helpful to the calibration of leaves in the visible wavelength range. This is a meaningful and a breakthrough exploration for HSL.

Radiometric Calibration for Incidence Angle, Range and Sub-Footprint Effects on Hyperspectral LiDAR Backscatter Intensity

Terrestrial hyperspectral LiDAR (HSL) sensors could provide not only spatial information of the measured targets but also the backscattered spectral intensity signal of the laser pulse. The raw intensity collected by HSL is influenced by several factors, among which the range, incidence angle and sub-footprint play a significant role. Further studies on the influence of the range, incidence angle and sub-footprint are needed to improve the accuracy of backscatter intensity data as it is important for vegetation structural and biochemical information estimation. In this paper, we investigated the effects on the laser backscatter intensity and developed a practical correction method for HSL data. We established a laser ratio calibration method and a reference target-based method for HSL and investigated the calibration procedures for the mixed measurements of the effects of the incident angle, range and sub-footprint. Results showed that the laser ratio at the red-edge and near-infrared laser wavelengths has higher accuracy and simplicity in eliminating range, incident angle and sub-footprint effects and can significantly improve the backscatter intensity discrepancy caused by these effects.

Potential of active multispectral lidar for detecting low reflectance targets

The calibration and sampling of the multispectral Light Detection and Ranging (lidar) intensity is still challenging because the data acquisition has to be optimized for simultaneous 3D measurement, and the intensity retrieval methods need to be fast to enable real-time detection. We have studied the spectral measurement of low reflectance targets with an 8-channel hyperspectral lidar with improved waveform sampling and sensitivity, which now allow the detection of spectral differences even at low reflectance values. Our initial analysis resulted in a classification accuracy greater than 80%, which indicates that the multispectral lidar is able to detect the small differences in target spectral properties when reflectance at two or more channels is compared at the same time.

Four Dimensional Mapping of Vegetation Moisture Content Using Dual-Wavelength Terrestrial Laser Scanning

Recently, terrestrial laser scanning (TLS) has shown potential in measuring vegetation biochemical traits in three dimensions (3D) by using reflectance derived from backscattered intensity data. The 3D estimates can provide information about the vertical heterogeneity of canopy biochemical traits which affects canopy reflectance but cannot be measured from spaceborne and airborne optical remote sensing data. Leaf equivalent water thickness (EWT), a metric widely used in vegetation health monitoring, has been successfully linked to the normalized difference index (NDI) of near and shortwave infrared wavelengths at the leaf level. However, only two previous studies have linked EWT to NDI at the canopy level in field campaigns. In this study, an NDI consisting of 808 and 1550 nm wavelengths was used to generate 3D EWT estimates at the canopy level in a broadleaf mixed-species tree plot during and after a heatwave. The relative error in EWT estimates was 6% across four different species. Temporal changes in EWT were measured, and the accuracy varied between trees, a factor of the errors in EWT estimates on both dates. Vertical profiles of EWT were generated for six trees and showed vertical heterogeneity and variation between species. The change in EWT vertical profiles during and after the heatwave differed between trees, demonstrating that trees reacted in different ways to the drought condition.

Hyperspectral lidar point cloud segmentation based on geometric and spectral information

Light detection and ranging (lidar) can record a 3D environment as point clouds, which are unstructured and difficult to process efficiently. Point cloud segmentation is an effective technology to solve this problem and plays a significant role in various applications, such as forestry management and 3D building reconstruction. The spectral information from images could improve the segmentation result, but suffers from the varying illumination conditions and the registration problem. New hyperspectral lidar sensor systems can solve these problems, with the capacity to obtain spectral and geometric information simultaneously. The former segmentation on hyperspectral lidar were mainly based on spectral information. The geometric segmentation method widely used by single wavelength lidar was not employed for hyperspectral lidar yet. This study aims to fill this gap by proposing a hyperspectral lidar segmentation method with three stages. First, Connected-Component Labeling (CCL) using the geometric information is employed for base segmentation. Second, the output components of the first stage are split by the spectral difference using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Third, the components of the second stage are merged based on the spectral similarity using Spectral Angle Match (SAM). Two indoor experimental scenes were setup for validation. We compared the performance of our mothed with that of the 3D and intensity feature based method. The quantitative analysis indicated that, our proposed method improved the point-weighted score by 19.35% and 18.65% in two experimental scenes, respectively. These results showed that the geometric segmentation method for single wavelength lidar could be combined with the spectral information, and contribute to the more effective hyperspectral lidar point cloud segmentation.

The terrestrial laser scanning revolution in forest ecology

New laser scanning technologies are set to revolutionize the way in which we measure and understand changes in ecosystem structure and function. Forest ecosystems present a particular challenge because of their scale, complexity and structural dynamics. Traditional forestry techniques rely on manual measurement of easy-to-measure characteristics such as tree girth and height, along with time-consuming, logistically difficult and error-prone destructive sampling. Much more detailed and accurate three-dimensional measurements of forest structure and composition are key to reducing errors in biomass estimates and carbon dynamics and to better understanding the role of forests in global ecosystem and climate change processes. Terrestrial laser scanners are now starting to be deployed in forest ecology research and, at the same time, new terrestrial laser scanning (TLS) technologies are being developed to enhance and extend the range of measurements that can be made. These new TLS measurements provide a tantalizing glimpse of a completely new way to measure and understand forest structure. It is therefore a good time to take stock, assess the state of the art and identify the immediate challenges for continued development of TLS in forest ecology.