Hyperspectral LiDAR: Understanding the Geometric Radiative Effects

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Hyperspectral LiDAR

The hyperspectral light detection and ranging system, better known as Hyperspectral LiDAR, stands as a notable breakthrough in imaging spectroscopy, furnishing researchers with both geometric and spectral data in a single capture. However, the technology is currently challenged by two significant geometric radiative effects, namely the distance effect and incidence angle effect, which potentially hampers its application in quantitative remote sensing. This article dives into the exploration and mitigation of these radiative effects by a research team led by Prof. Niu Zheng at the Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS).

Exploring and Correcting Distance Effects in Hyperspectral LiDAR

The team first zeroed in on the distance effect, noting that this effect originates from the system itself, and impacts all wavelengths in a uniform manner. To counteract this effect, they devised a piecewise function model which integrates a quadratic function and an exponential decay function. After applying this model, they were able to analyze and correct the distance effect present in Hyperspectral LiDAR. Further details on this innovative approach can be found in the ISPRS Journal of Photogrammetry and Remote Sensing and IEEE Transactions on Geoscience and Remote Sensing.

Addressing the Incidence Angle Effect on Vegetation Targets

The researchers then turned their attention to the incidence angle effect, particularly as it pertains to various types of vegetation leaf targets. They observed that this effect varied depending on the surface microscopic physical structures and internal biochemical parameters of different leaf types. To address this, they developed a new version of the Poullain algorithm to correct the incidence angle effect of the target. Unlike the Lambert cosine law based on isotropic scattering assumptions and the original Poullain algorithm, this improved algorithm considers the variability of the target roughness factor and diffuse reflection coefficient under different incidence angles and wavelengths.

Validation and Application of the Algorithms

The team then tested their algorithms by comparing the standard deviations of the correction results with the echo intensity and reflectivity under a standard 0-degree incidence angle. They found a significant reduction in the standard deviations, ranging between 30% to 60%, thereby confirming the efficacy of their methods. As a result, their research provides an important theoretical foundation and technical support for the accurate inversion of 3D biochemical parameters of vegetation in the future.

The Future of Hyperspectral LiDAR Systems

At present, the research team is working on the second generation of the hyperspectral LiDAR system, with a focus on improving its high-speed acquisition capabilities. They are currently in the process of testing its performance and expect to put it into use by the end of 2023. The promise of this new system lies in its potential to make quantum leaps in the field of remote sensing, thereby benefiting a range of applications, from environmental science to defense intelligence.

Advancing Hyperspectral LiDAR Technology

The challenges presented by the geometric radiative effects in Hyperspectral LiDAR, namely the distance and incidence angle effects, potentially limit its utility in quantitative remote sensing. The pioneering work led by Prof. Niu Zheng and his team at AIR, CAS, marks a significant stride forward in overcoming these challenges.

By proposing a novel piecewise function model and an improved version of the Poullain algorithm, they have effectively established a means to analyze and correct these two major geometric radiative effects. Their research findings, validated through empirical testing, hold considerable implications for enhancing the accuracy and efficiency of Hyperspectral LiDAR systems.

As we look to the future, the development of the second generation of Hyperspectral LiDAR systems is underway, set to bring about greater high-speed acquisition capabilities. The success of these endeavors will undoubtedly further cement the role of Hyperspectral LiDAR as an invaluable tool in a myriad of applications, ranging from environmental monitoring to defense intelligence.

The implications of this study extend far beyond the immediate scope of remote sensing technology. As our understanding and control of Hyperspectral LiDAR improves, so too does our ability to study and interact with the world around us. In essence, this research provides the key to unlocking a new realm of possibilities in the realm of imaging spectroscopy and 3D sensing.

In conclusion, the research carried out by Prof. Niu Zheng and his team represents a significant breakthrough in the field of Hyperspectral LiDAR. Their tireless efforts to analyze and mitigate the geometric radiative effects present in Hyperspectral LiDAR are paving the way for the technology’s future, promising a future where accurate and efficient hyperspectral imaging is commonplace.

Read Original Article: https://ieeexplore.ieee.org/document/9404348


  1. Niu, Z., Zhang, W., Wang, C., & Guo, Z. (2023). A novel approach to correcting geometric radiative effects in hyperspectral LiDAR. ISPRS Journal of Photogrammetry and Remote Sensing.
  2. Niu, Z., Li, S., Guo, Z., & Wang, C. (2023). Improved Poullain algorithm for correcting incidence angle effect in hyperspectral LiDAR. IEEE Transactions on Geoscience and Remote Sensing.
  3. LiDAR Magazine. (2023). Hyperspectral LiDAR: An Introduction. Retrieved from https://lidarmag.com/2023/01/01/hyperspectral-lidar-an-introduction.
  4. Aerospace Information Research Institute. (2023). Research Progress. Retrieved from http://air.cas.cn.
  5. Institute of Electrical and Electronics Engineers. (2023). IEEE Transactions on Geoscience and Remote Sensing. Retrieved from https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36.

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