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.

A Quadratic Model with Nonpolynomial Terms for Remote Colorimetric Calibration of 3D Laser Scanner Data Based on Piecewise Cubic Hermite Polynomials

The processing of intensity data from terrestrial laser scanners has attracted considerable attention in recent years. Accurate calibrated intensity could give added value for laser scanning campaigns, for example, in producing faithful 3D colour models of real targets and classifying easier and more reliable automatic tools. In cultural heritage area, the purely geometric information provided by the vast majority of currently available scanners is not enough for most applications, where indeed accurate colorimetric data is needed. This paper presents a remote calibration method for self-registered RGB colour data provided by a 3D tristimulus laser scanner prototype. Such distinguishing colour information opens new scenarios and problems for remote colorimetry. Using piecewise cubic Hermite polynomials, a quadratic model with nonpolynomial terms for reducing inaccuracies occurring in remote colour measurement is implemented. Colorimetric data recorded by the prototype on certified diffusive targets is processed for generating a remote Lambertian model used for assessing the accuracy of the proposed algorithm. Results concerning laser scanner digitizations of artworks are reported to confirm the effectiveness of the method.

Surface Shape Measurement Using Extended Photometric Stereo

Photometric stereo is a widely-used non-contact optical technology for surface shape measurement in industry. However, it is subject two limitations: (1) all light directions used should be known; (2) reflection on the surface to be measured should obey the Lambertian model. In this paper, an extended photometric stereo is proposed to overcome these limitations. Firstly, initial light directions are estimated in terms of normals of those points on the silhouette; secondly, an iterative process is established to refine alternately both the normals and all light directions, with specular pixels removed; at last, the final surface shape is worked out by integrating the normals. Experimental results show the considerable feasibility of this algorithm.

Enhanced 3D Shape Recovery Using the Neural-Based Hybrid Reflectance Model

It is known that most real surfaces usually are neither perfectly Lambertian model nor ideally specular model; rather, they are formed by the hybrid structure of these two models. This hybrid reflectance model still suffers from the noise, strong specular, and unknown reflectivity conditions. In this article, these limitations are addressed, and a new neural-based hybrid reflectance model is proposed. The goal of this method is to optimize a proper reflectance model by learning the weight and parameters of the hybrid structure of feedforward neural networks and radial basis function networks and to recover the 3D object shape by the shape from shading technique with this resulting model. Experimental results, including synthetic and real images, were performed to demonstrate the performance of the proposed reflectance model in the case of different specular effects and noise environments.