Practical photoquantity measurement using a camera

International audienceAn image output by a camera is generally not a faithful representation of the real scene, because it undergoes a series of radiometric disturbances during the imaging process. This paper proposes a method for obtaining a more accurate measure of the light seen by a camera. Our method requires no specific calibration apparatus and only minimal supervision. Nevertheless, it is quite comprehensive, since it accounts for response function, exposure, vignetting, spatial non-uniformity of the sensor and colour balancing. Our method works in two steps. First, the camera is calibrated off-line, in a photoquantity sense. Then, the photoquantity of any scene can be estimated in-line. Our method is therefore geared to a wide range of computer vision applications where a camera is expected to give a measurement of the visible light. The paper starts by presenting a photoquantity model of the camera imaging process. It then describes the key steps of calibration and correction method. Finally, results are given and analyzed to evaluate the relevance of our approach

Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor

Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R-2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R-2 was 0.80-0.97 for the red-edge, near-infrared, and red bands

Procedures for Correcting Digital Camera Imagery Acquired by the AggieAir Remote Sensing Platform

Developments in sensor technologies have made consumer-grade digital cameras one of the more recent tools in remote sensing applications. Consumer-grade digital cameras have been the imaging sensor of choice by researchers due to their small size, light weight, limited power requirements, and their potential to store hundreds of images (Hardin 2011). Several studies have focused on the use of digital cameras and their efficacy in remote sensing applications. For satellite and airborne multispectral imaging systems, there is a well established radiometric processing approach. However, radiometric processing lines for digital cameras are currently being researched. The goal of this report is to describe an absolute method of radiometric normalization that converts digital numbers output by the camera to reflectance values that can be used for remote sensing applications. This process is used at the AggieAir Flying Circus (AAFC), a service center at the Utah Water Research Laboratory at Utah State University. The AAFC is a research unit that specializes in the acquisition, processing, and interpretation of aerial imagery obtained with the AggieAirTM platform. AggieAir is an autonomous, unmanned aerial vehicle system that captures multi-temporal and multispectral high resolution imagery for the production of orthorectified mosaics. The procedure used by the AAFC is based on methods adapted from Miura and Huete (2009), Crowther (1992) and Neale and Crowther (1994) for imagery acquired with Canon PowerShot SX100 cameras. Absolute normalization requires ground measurements at the time the imagery is acquired. In this study, a barium sulfate reflectance panel with absolute reflectance is used. The procedure was demonstrated using imagery captured from a wetland near Pleasant Grove, Utah, that is managed by the Utah Department of Transportation

Standardized spectral and radiometric calibration of consumer cameras

Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database