A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration
By Alireza G. Kashani, M. Olsen, Christopher E. Parrish, Nicholas Wilson. Italian National Conference on Sensors, 2015
336 citations recorded by the source corpus.
In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.
Subfields: Environmental Engineering, Geology, Instrumentation
View Paper: https://www.semanticscholar.org/paper/5ae6b96aa0c3848dc321d219501d3429bcf28bc5
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In Environmental Engineering
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In Geology
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Citations in the corpus
- LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World (2020, neutral): “6, left)[19].”
- Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection (2020, background): “intensity readings vary for different objects [6], e.”
- Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery (2021, background): “, airborne or satellite imagery data [13–15], (ii) the ones that exploit height information (LiDAR) [16,17], and (iii) those that combine both of data sources [18,19].”
- BirdNet: A 3D Object Detection Framework from LiDAR Information (2018, background): “As can be seen on Table III, the least relevant information set for our detection and classification architecture corresponds to intensity values acquired by the LiDAR, as it might be expected due to many factors affecting reflectance measurements [22].”