Refining the Geometric Calibration of a Hiperspectral Frame Camera with Preliminary Bands Coregistration

Tommaselli, Antônio Maria Garcia; Santos, Lucas Dias; Oliveira, Raquel Alves de; Honkavaara, Eija

doi:10.1109/igarss.2018.8519058

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…The first one, as addressed by is to estimate the IOPs for each single band. Another approach, as proposed by Tommaselli et al (2018) is to estimate a set of 2D transformations with the camera static, and then using this transformations to coregister all bands. Honkavaara et al, (2017) developed an approach for accurate band registration utilizing a few FPI-camera reference bands and an accurate object model.…”

Section: Methodsmentioning

confidence: 99%

A Study on the Variations of Inner Orientation Parameters of a Hyperspectral Frame Camera

Tommaselli

Santos

Berveglieri

et al. 2018

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

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Abstract. New low-cost hyperspectral frame sensors have created a new perspective for remote sensing applications. In this work, we investigate some issues related to the geometric calibration of a hyperspectral frame camera based of FPI (Fabry-Pérot Interferometer), the Rikola camera. The approach proposed in paper is to study the changes in internal optical path caused by the FPI and by the splitting prism. The aim is to model the changes in the IOPs with an analytical function and also to estimate the misalignments between sensors. Several experiments were performed. The changes in position of a specific point were analasyzed to confirm that the bundle of rays is deviated. A self-calibrating bundle adjustment was performed and the Interior Orientation Parameters (IOP) of each band were estimated. The IOPs were analysed and it was concluded that a single set of symmetrical radial distortion parameters can be used for all band. Also, the estimated parameters for each image band were analysed as a function of the air gap of the FPI interferometer. It was noticed some correlation between the focal length and the air gap, and an air-gap dependent model was estimated. Thus, instead of considering an IOP set for each band or for each sensor, a single set of distortion parameters and another set of parameters that is “air-gap dependent”, was assessed. Another important issue was the determination of the misalignment angles between the two sensors, which can explain some differences in the recovered camera trajectory when performing the bundle adjustment.

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Section: Methodsmentioning

confidence: 99%

A Study on the Variations of Inner Orientation Parameters of a Hyperspectral Frame Camera

Tommaselli

Santos

Berveglieri

et al. 2018

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

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“…The data acquisition was performed by a hyperspectral camera (Senop Rikola) and a multispectral camera (MAPIR Survey 3N). Senop Rikola hyperspectral camera is a snapshot camera based on a Fabry-Perot Interferometer [24,25]. It includes two not aligned sensors: one sensor acquires near-infrared bands (659.2-802.6 nm) and the second captures visible bands (from 502.8 to 635.1 nm).…”

Section: Sensor Descriptionmentioning

confidence: 99%

Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images

Musci

Mazzara

Lingua

2020

Drones

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Aircraft ground de-icing operations play a critical role in flight safety. However, to handle the aircraft de-icing, a considerable quantity of de-icing fluids is commonly employed. Moreover, some pre-flight inspections are carried out with engines running; thus, a large amount of fuel is wasted, and CO2 is emitted. This implies substantial economic and environmental impacts. In this context, the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of Ice) aims to provide innovative tools to identify the ice on aircraft and improve the efficiency of the de-icing process. The project includes the design of a low-cost UAV (uncrewed aerial vehicle) platform and the development of a quasi-real-time ice detection methodology to ensure a faster and semi-automatic activity with a reduction of applied operating time and de-icing fluids. The purpose of this work, developed within the activities of the project, is defining and testing the most suitable sensor using a radiometric approach and machine learning algorithms. The adopted methodology consists of classifying ice through spectral imagery collected by two different sensors: multispectral and hyperspectral camera. Since the UAV prototype is under construction, the experimental analysis was performed with a simulation dataset acquired on the ground. The comparison among the two approaches, and their related algorithms (random forest and support vector machine) for image processing, was presented: practical results show that it is possible to identify the ice in both cases. Nonetheless, the hyperspectral camera guarantees a more reliable solution reaching a higher level of accuracy of classified iced surfaces.

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“…This camera is based on tunable filters able to inspect spectral range between 500-900 nm, including two sensors: one sensor (defined as Sensor 1) acquires near infrared bands, from 659.2 nm to 802.6 nm, while the second (Sensor 2) captures visible bands, from 502.8 nm to 635.1 nm. Among the different components of this camera, one of the most important is the Fabry-Perot Interferometer (FPI): this interferometer is composed by two partially reflective parallel plates with variable distance (air gap), controlled by piezoelectric actuators (Saari et al, 2009;Tommaselli et al, 2018). When the electromagnetic radiation affects the plates, many refractions and reflections occur: the constructive interferences that happen within the plates allow certain wavelengths to be transmitted while others are reflected, because the wavelengths are function of FPI gap (air-gap).…”

Section: Intruductionmentioning

confidence: 99%

Reliability of the Geometric Calibration of an Hyperspectral Frame Camera

Musci

Aicardi

Dabove

et al. 2019

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

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Abstract. One of the main tools for high resolution remote sensing and photogrammetry is the lightweight hyperspectral frame camera, that is used in several application areas such as precision agriculture, forestry, and environmental monitoring. Among these types of sensors, the Rikola (which is based on a Fabry–Perot interferometer (FPI) and produced by Senop) is one of the latest innovations. Due to its internal geometry, there are several issues to be addressed for the appropriate definition and estimation of the inner orientation parameters (IOPs). The main problems concern the possibility to change every time the sequence of the bands and to assess the reliability of the IOPs. This work focuses the attention on the assessment of the IOPs definition for each sensor, considering the impact of environmental conditions (e.g., different time, exposure, brightness) and different configurations of the FPI camera, in order to rebuild an undistorted hypercube for image processing and object estimation. The aim of this work is to understand if the IOPs are stable over the time and if and which bands can be used as reference for the calculation of the inner parameters for each sensor, considering different environmental configurations and surveys, from terrestrial to aerial applications. Preliminary performed tests showed that the focal length percentage variation among the bands of different experiments is around 1%.

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Refining the Geometric Calibration of a Hiperspectral Frame Camera with Preliminary Bands Coregistration

Cited by 4 publications

References 4 publications

A Study on the Variations of Inner Orientation Parameters of a Hyperspectral Frame Camera

A Study on the Variations of Inner Orientation Parameters of a Hyperspectral Frame Camera

Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images

Reliability of the Geometric Calibration of an Hyperspectral Frame Camera

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