Abstract
Hyperspectral line-scan cameras are increasingly being deployed on mobile platforms operating in unstructured environments. To generate geometrically accurate hyperspectral composites, the intrinsic parameters of these cameras must be resolved. This article describes a method for determining the intrinsic parameters of a hyperspectral line-scan camera. The proposed method is based on a cross-ratio invariant calibration routine and is able to estimate the focal length, principal point, and radial distortion parameters in a hyperspectral line-scan camera. Compared to previous methods that use similar calibration targets, our approach extends the camera model to include radial distortion. It is able to utilize calibration data recorded from multiple camera view angles by optimizing the re-projection error of all calibration data jointly. The proposed method also includes an additional signal processing step that automatically detects calibration points in hyperspectral imagery of the calibration target. These contributions result in accurate estimates of the intrinsic parameters with minimal supervision. The proposed method is validated through comprehensive simulation and demonstrated on real hyperspectral line-scans.
Highlights
Hyperspectral line-scan cameras have been widely used by agricultural robots, e.g., the Ladybird robot as shown in Figure 1, for various applications such as fruit detection [1], weed detection [2,3,4], nutrient status estimation [5], pest surveillance [6], discolouration detection [7], damage detection [8], and yield estimation [9]
We present an improved cross-ratio invariant-based intrinsic calibration method that is designed to estimate the intrinsic parameters of a hyperspectral line-scan camera including the principal point, focal length, and radial distortion of the lens
The Resonon Pika XC-2 visible to near infrared (VNIR) hyperspectral line-scan camera is mounted on top of the Ladybird platform and oriented such that the scan line is horizontal and pitched down for scanning the ground surface
Summary
Hyperspectral line-scan cameras have been widely used by agricultural robots, e.g., the Ladybird robot as shown in Figure 1, for various applications such as fruit detection [1], weed detection [2,3,4], nutrient status estimation [5], pest surveillance [6], discolouration detection [7], damage detection [8], and yield estimation [9]. Hyperspectral line-scan cameras have been widely used by agricultural robots, e.g., the Ladybird robot as shown, for various applications such as fruit detection [1], weed detection [2,3,4], nutrient status estimation [5], pest surveillance [6], discolouration detection [7], damage detection [8], and yield estimation [9]. Hyperspectral line-scan cameras provide both high spatial and spectral resolution and high sample rate images. Producing hyperspectral images of a scene is a three-dimensional problem. A hyperspectral image provides much more resolution along the spectral dimension compared to panchromatic sensors [10]. Two dimensions are required to record spatial information. The third dimension is used to record layers of spectral information. Optical systems work by gathering light through a series of lenses and projecting the light onto a two dimensional imaging plane. Capturing the extra dimension of spectral information raises a design challenge
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