A new coarse-to-fine rectification algorithm for airborne push-broom hyperspectral images

Tuo, Hongya; Liu, Yuncai

doi:10.1016/j.patrec.2005.02.005

Cited by 13 publications

(5 citation statements)

References 1 publication

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“…For indirect georectification methods, features from a reference image are used and matched with features of the acquired lines to project them on the ground [36, 44,45]. Therefore, it is not required to use navigation sensors information.…”

Section: Georectificationmentioning

confidence: 99%

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

Freitas

Silva

Almeida

et al. 2017

J Intell Robot Syst

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This work address hyperspectral imaging systems use for maritime target detection using unmanned aerial vehicles. Specifically, by working in the creation of a hyperspectral real-time data processing system pipeline. We develop a boresight calibration method that allows to calibrate the position of the navigation sensor related to the camera imaging sensor, and improve substantially the accuracy of the target geo-reference. We also develop an unsupervised method for segmenting targets (boats) from their dominant background in real-time. We evaluated the performance of our proposed system for target detection in real-time with UAV flight data and present detection results comparing favorably our approach against other state-ofthe-art method.

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Section: Georectificationmentioning

confidence: 99%

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

Freitas

Silva

Almeida

et al. 2017

J Intell Robot Syst

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“…Hyperspectral imaging from airplanes using push broom technique [24] has been used in the last decade. The push broom technique and instrumentation used in this study is explained in Appendix A (Fig.…”

Section: Hyperspectral Imager and Image Processingmentioning

confidence: 99%

Kelp forest mapping by use of airborne hyperspectral imager

Volent

Johnsen

Sigernes

2007

J. Appl. Remote Sens

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We present an easy and efficient approach for remote sensing of ocean color, relevant for monitoring and management of kelp forest and bottom substrate with a cheap custom made hyperspectral imager. Remote sensing of ocean color was performed in the Kongsfjord, Spitsbergen (79° N and 12° E) from an airplane (2950 m altitude) equipped with a hyperspectral imager, giving monochromatic images (425-825 nm) using the push broom technique, captured with custom designed software in 5 nm steps. Synchronously in situ measurements of upwelling spectral irradiance, (E u (λ)) (λ = 350-950 nm) measured at 30 cm depth were performed as a reference for the remotely sensed images. Surface water samples were taken for enumeration and identification of organic (plankton), inorganic particles, and colored dissolved organic matter. For identification and classification of kelp and bottom substrate, Bayesian supervised classification and a differential histogram equalization technique were used and compared. Both techniques gave successful discrimination between kelp and bottom substrate in shallow water above the Secchi depth (<19 m). The imager could easily be implemented for other applications such as detection and monitoring of phytoplankton blooms, suspended matter, and colored dissolved organic matter in surface waters, especially in connection with environmental and aquaculture management.

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“…However, the precision obtained by this method is affected by the quality of the navigation data [2], as well as the intrinsic calibration of the imaging sensor [3]. Due to this, the ground error of the georeferencing can be several metres to tens of metres, depending on the altitude of the aircraft and the Ground 2 of 21 Sampling Distance (GSD) [4,5]. To improve the quality of the georeferencing, several ancillary data can complement the navigation data.…”

Section: Introductionmentioning

confidence: 99%

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

Barbieux¹

2018

Preprint

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Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties: it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustement step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection; finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection which RMSE with the reference orthophoto is around two pixels.

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A new coarse-to-fine rectification algorithm for airborne push-broom hyperspectral images

Cited by 13 publications

References 1 publication

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

Kelp forest mapping by use of airborne hyperspectral imager

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

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