A geometric model for linear‐array‐based terrestrial panoramic cameras

Schneider, D.; Maas, Hans‐Gerd

doi:10.1111/j.1477-9730.2006.00384.x

Cited by 46 publications

(46 citation statements)

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“…The model allows 3D object space points to be projected into image space and the inverse of this operation, i.e., the creation of 3D object space rays emanating from the camera, for any desired image pixel. To date, most of the terrestrial HSI applied to geologic studies has been collected with panoramic linear array sensors that rotate around a vertical axis intersecting the camera optical centerline, thus forming a standard cylindrical model [5,24]. However, the optical centerline of the SWIR camera used in this work is offset from the stage rotation axis by approximately 20 cm due to the simultaneous presence of both the VNIR and SWIR cameras on the stage (see Figure 2).…”

Section: Hsimentioning

confidence: 99%

Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion

2017

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Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures. Although solar shadowing is prevalent in terrestrial HSI due to the vertical scene geometry, automated shadow detection and restoration algorithms have not yet been applied to this capture modality. We investigate the fusion of terrestrial laser scanning (TLS) spatial information with terrestrial HSI for geometric shadow detection on a rough vertical surface and examine the contribution of radiometrically calibrated TLS intensity, which is resistant to the influence of solar shadowing, to HSI shadow restoration. Qualitative assessment of the shadow detection results indicates pixel level accuracy, which is indirectly validated by shadow restoration improvements when sub-pixel shadow detection is used in lieu of single pixel detection. The inclusion of TLS intensity in existing shadow restoration algorithms that use regions of matching material in sun and shade exposures was found to have a marginal positive influence on restoring shadow spectrum shape, while a proposed combination of TLS intensity with passive HSI spectra boosts restored shadow spectrum magnitude precision by 40% and band correlation with respect to a truth image by 45% compared to existing restoration methods.

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Section: Hsimentioning

confidence: 99%

Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion

2017

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“…Because rotation is required to build up the number of image columns, the width of an image is variable, determined by the user. This configuration results in panoramic imaging geometry, where only the across-track image direction can be represented by central perspective projection (Luhmann et al, 2006;Schneider and Maas, 2006). Spatial resolution is low compared with a contemporary digital camera, but the high spectral resolution allows greater differentiation of mineral content.…”

Section: Instrumentation and Data Collectionmentioning

confidence: 99%

“…Previous work (Kurz et al, 2011) has shown that the geometric model for the HySpex sensor can be represented by a standard model for linear-array panoramic cameras (Schneider and Maas, 2006). Additional parameters defined in this model account for the horizontal pixel size defined by the rotation step, and the non-parallelism of the sensor line and rotation axis.…”

Section: Processingmentioning

confidence: 99%

The Benefits of Terrestrial Laser Scanning and Hyperspectral Data Fusion Products

Buckley

Kurz

Schneider

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Close range hyperspectral imaging is a developing method for the analysis and identification of material composition in many applications, such as in within the earth sciences. Using compact imaging devices in the field allows near-vertical topography to be imaged, thus bypassing the key limitations of viewing angle and resolution that preclude the use of airborne and spaceborne platforms. Terrestrial laser scanning allows 3D topography to be captured with high precision and spatial resolution. The combination of 3D geometry from laser scanning, and material properties from hyperspectral imaging allows new fusion products to be created, adding new information for solving application problems. This paper highlights the advantages of terrestrial lidar and hyperspectral integration, focussing on the qualitative and quantitative aspects, with examples from a geological field application. Accurate co-registration of the two data types is required. This allows 2D pixels to be linked to the 3D lidar geometry, giving increased quantitative analysis as classified material vectors are projected to 3D space for calculation of areas and examination of spatial relationships. User interpretation of hyperspectral results in a spatially-meaningful manner is facilitated using visual methods that combine the geometric and mineralogical products in a 3D environment. Point cloud classification and the use of photorealistic modelling enhance qualitative validation and interpretation, and allow image registration accuracy to be checked. A method for texture mapping of lidar meshes with multiple image textures, both conventional digital photos and hyperspectral results, is described. The integration of terrestrial laser scanning and hyperspectral imaging is a valuable means of providing new analysis methods, suitable for many applications requiring linked geometric and chemical information.

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“…Many hyperspectral imagers operate as pushbroom line sensors mounted on a rotation stage. These cameras have to be modelled as panorama cameras with cylindrical image geometry for photogrammetric processing (Schneider and Maas 2006), in contrast to conventional frame cameras. Hyperspectral imagery is registered to the lidar coordinate system measuring ground control points within an image either manually or in an automatic manner (Monteiro et al 2013, Sima et al 2014.…”

Section: Integration With Topographic Datamentioning

confidence: 99%

A Review of Hyperspectral Imaging in Close Range Applications

Kurz¹,

Buckley²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Hyperspectral imaging is an established method for material mapping, which has been conventionally applied from airborne and spaceborne platforms for a range of applications, including mineral and vegetation mapping, change detection and environmental studies. The main advantage of lightweight hyperspectral imagers lies in the flexibility to deploy them from various platforms (terrestrial imaging and from unmanned aerial vehicles; UAVs), as well as the high spectral resolution to cover an expanding wavelength range. In addition, spatial resolution allows object sampling distances from micrometres to tens of centimetrescomplementary to conventional nadir-looking systems. When this new type of imaging device was initially released, few instruments were available and the applicability and potential of the method was restricted. Today, a wider range of instruments, with a range of specifications, is available, with significant improvements over the first generation of technology. In this contribution, the state-ofthe-art of hyperspectral imaging will be reviewed from a close range measurement perspective, highlighting how the method supplements geometric modelling techniques. An overview of the processing workflow, adjusted to the more complex close range imaging scenario will be given. This includes the integration with 3D laser scanning and photogrammetric models to provide a geometric framework and real world coordinate system for the hyperspectral imagery.

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A geometric model for linear‐array‐based terrestrial panoramic cameras

Cited by 46 publications

References 1 publication

Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion

Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion

The Benefits of Terrestrial Laser Scanning and Hyperspectral Data Fusion Products

A Review of Hyperspectral Imaging in Close Range Applications

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