Hyperspectral reconnaissance in urban environment

Renhorn, Ingmar; Achard, Véronique; Axelsson, Maria; Benoist, Koen W.; Borghys, Dirk; Briottet, Xavier; Dekker, R.J.; Dimmeler, Alwin; Friman, Ola; Kåsen, Ingebjørg; Matteoli, Stefania; Moro, Maria Lo; Opsahl, Thomas Olsvik; Persie, M. van; Resta, Salvatore; Schilling, Hendrik; Schwering, Piet B. W.; Shimoni, Michal; Haavardsholm, Trym Vegard; Viallefont, Françoise

doi:10.1117/12.2019348

Cited by 8 publications

(5 citation statements)

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“…This characteristic yields more effective classification power for the application areas mentioned above. Spectral Signature is nothing but a different type of materials can be represented by a set of bands, this spectral signature simplifies the separation of these materials [14] [15].…”

Section: IImentioning

confidence: 99%

“…This is particularly the case in urban areas, which are dominated by complex regions and surface heterogeneity which often prevents the collection of reliable ground-truth samples. While the collection of samples is generally difficult, expensive and time-consuming, by using Hyperspectral data we can identify more objects and can generate more classes [15].…”

Section: Problemmentioning

confidence: 99%

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2018

IJET

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Section: IImentioning

confidence: 99%

Section: Problemmentioning

confidence: 99%

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2018

IJET

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“…persons, vehicles and objects). The processing covers classification, anomaly detection, change detection and spectral matching [3,4,5]. The processing used hyperspectral data from the visible light to the short wave infrared data and in the long wave infrared, broad band sensor data in the visible, near infrared and mid wave infrared and airborne 3D-laser scanner as well.…”

Section: Introductionmentioning

confidence: 99%

Combined airborne sensors in urban environment

et al. 2013

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Military operations in urban areas became more relevant in the past decades. Detailed situation awareness in these complex environments is crucial for successful operations. Within the EDA (European Defence Agency) project on "Detection in Urban scenario using Combined Airborne imaging Sensors" (DUCAS) an extensive data set of hyperspectral and high spatial resolution data as well as three dimensional (3D) laser data was generated in a common field trial in the city of Zeebrugge, Belgium, in the year 2011. In the frame of DUCAS, methods were developed at two levels of processing. In the first level, single sensor data were used for land cover mapping and the detection of targets of interest (i.e. personnel, vehicles and objects). In the second level, data fusion was applied at pixel level as well as information level to investigate the benefits of combining sensor systems in an operational context. Providing data for mission planning and mapping is an important task for aerial reconnaissance and it includes the creation or the update of high quality 2D and 3D maps. In DUCAS, semi-automatic methods and a wide range of sensor data (hyperspectral, LIDAR, high resolution orthophotos and video data) were used for the creation of highly detailed land cover maps as well as urban terrain models. Combining the diverse information gained by different sensors increases the information content and the quality of the extracted information. In this paper we will present advanced methods for the creation of 2D/3D maps, show results and the benefit of fusing multi-sensor data

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“…Since the spectral signature depends on the spatial resolution, especially for textured surfaces, the information content can be higher when the spatial resolution is given priority. 1 The optimal trade-off depends on target range and target size as well as the spectral variability of the target and background materials. Solid materials mostly exhibit slow spectral variations and can, therefore, be more sparsely sampled.…”

Section: Introductionmentioning

confidence: 99%

High spatial resolution hyperspectral camera based on a linear variable filter

Renhorn¹,

Bergström

Hedborg

et al. 2016

Opt. Eng

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Abstract. A small, lightweight, and inexpensive hyperspectral camera based on a linear variable filter close to the focal plane array (FPA) is described. The use of a full-frame sensor allows large coverage with high spatial resolution at moderate spectral resolution. The spatial resolution has been maintained using a tilt/shift lens for chromatic focusing corrections. The trade-offs of positioning the filter relative to the FPA and varying the f -number have been studied. Calibration can correct for artifacts such as spectral filter variability. Reference spectra can be obtained using the same camera system by imaging targets over homogeneous areas. For textured surfaces, the different materials can be separated by using statistical methods. Accurate reconstruction of the sparse spectral image data is demonstrated.

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Hyperspectral reconnaissance in urban environment

Cited by 8 publications

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Combined airborne sensors in urban environment

High spatial resolution hyperspectral camera based on a linear variable filter

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