Lidar Intensity Information: A Review of Applications and Processing Techniques

In addition to precise 3D coordinates most lidar systems also record “intensity”. To date, intensity data have proven beneficial in data registration, feature extraction, classification, surface analysis, and segmentation to name just a few examples. The list of applications also continues to grow rapidly, as lidar researchers and practitioners develop new and innovative uses of these data.

The primary benefit of lidar intensity lies in the fact that it is related to surface reflectance and other surface characteristics; However, there are also a number of confounding variables to which intensity is related, including parameters related to the data acquisition geometry (e.g. range and angle of incidence), scanning environment, and sensors, themselves. To overcome this issue, several intensity processing techniques have been developed and implemented to calibrate, normalize, or otherwise correct the recorded intensity values to produce values that are more useful and more closely related to true surface characteristics.

Currently, there is very little consistency across lidar radiometric processing efforts. Multiple individuals, groups, and organizations are applying vastly different processing approaches and using differing terminology to describe these procedures. Also, it is not always clear what adjustments have been applied to intensity information in a particular dataset.

In a paper[i] published in the Sensors journal, our Geomatics research lab at Oregon State University provided an overview of effective parameters on intensity measurements, basic theory, applications, and current intensity processing methods. We also defined terminology adopted from the most commonly-used conventions based on a review of current literature. While the intent of this paper was to focus primarily on topographic lidar, we provided a brief treatment of corresponding processing of bathymetric lidar to illuminate the similarities and differences between the two.

For the purpose of this paper, we distinguished four levels of intensity processing and reviewed current related methods. Each level increases not only with respect to the accuracy and quality of information but also in effort required:

  • Level 0: No modification (raw intensity): These are the basic intensity values directly provided by the manufacturer or vendor in their native storage format. They are typically scaled to values of 0–1 (floating point), 0–255 (8-bit integer), or 0–65,535 (16-bit integer), depending on the manufacturer. However, the processes used for scaling the sensor voltages and any adjustments applied are often unknown. Similar results can be obtained for the same scanner model by the same manufacturer; however, there typically is no direct agreement or relationship between values provided by different systems or manufacturers.
  • Level 1: Intensity correction: In this process an adjustment is made to the intensity values to reduce or ideally eliminate variation caused by one or more effective parameters (e.g., range, angle of incidence, etc.). This process is performed by either a theoretical or empirical correction model. Intensity correction ultimately can result in pseudo-reflectance values.
  • Level 2: Intensity normalization: In this process an intensity image is normalized through scaling to adjust the contrast and/or a shift to adjust the overall “brightness” to improve matching with a neighboring tile or overlapping strip (i.e., a histogram matching or normalization).
  • Level 3: Rigorous radiometric correction and calibration: In this meticulous process, the intensity values from the lidar system are first evaluated on targets with known reflectance, resulting in the determination of calibration constants for the sensor. The calibration constants are then applied to future data that are collected with the system including additional Level 1 intensity corrections to account for any deviations in parameters (e.g., range, angle of incidence). When completed rigorously, this process results in “true” reflectance information. Hence, when radiometric calibration has been applied, consistent data can be obtained from different systems, operated with different parameters settings, and in different conditions. In this paper, we refer to these as reflectance values.

While it is hoped that this paper will prove useful to a broad range of users, the primary intended audience consists of practitioners who want to evaluate different radiometric processing approaches from an implementation perspective and/or lidar data consumers who want to better understand (and possibly control, through appropriate contract wording) the types of intensity-derived products that are delivered by lidar service providers.

To view the paper full-text please visit: (Open Access)

Alireza G. Kashani, PhD

Postdoctoral Scholar

School of Civil and Construction Engineering

Oregon State University

[i] Kashani, A. G., Olsen, M. J., Parrish, C. E., & Wilson, N. (2015). A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration. Sensors15(11), 28099-28128.

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