Low‐altitude Terrestrial Spectroscopy from a Pushbroom Sensor

Ramirez-Paredes, Juan-Pablo; Lary, David J.; Gans, Nicholas

doi:10.1002/rob.21624

Cited by 14 publications

(20 citation statements)

References 39 publications

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“…To alleviate reliance on GCPs, a significant amount of contributions make use of the co-acquisition of frame images, and develop methods to co-register the frame images and the scan lines. The most sophisticated algorithm seems to be given by [20]: the interior orientation of the pushbroom scanner is calibrated and the sensor is aligned with the frame camera using a target not parallel to the plane of the CCD array, and then the stitching of the frame images allows the construction of the hyperspectral mosaic. Additionally, this method does not require any navigation data, however, it supposes either the calibration with the triangle target prior to every flight, or the stability of the relative orientation of the two sensors.…”

Section: State Of the Artmentioning

confidence: 99%

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

Barbieux

2018

Remote Sensing

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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 adjustment 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 with an RMSE, with the reference orthophoto, around two pixels.

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Section: State Of the Artmentioning

confidence: 99%

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

Barbieux

2018

Remote Sensing

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“…The geometric correction can be divided into image-to-map correction and image-to-image correction. Image-to-map correction registers the images in question to existing reference data, such as the Digital Surface Model (DSM) [8][9][10][11] or Ground Control Points (GCPs) [12,13]. Image-to-image correction recoveries position and pose relations of images using tie points among the images [14][15][16], and is widely used in UAV data processing methods [17].…”

Section: Introductionmentioning

confidence: 99%

A Simplified Method for UAV Multispectral Images Mosaicking

et al. 2017

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This paper presents a method for mosaicking unmanned aerial vehicle (UAV) multispectral images. The main purpose of the proposed method is to reduce spatial distortion in the mosaicking process and increase robustness and the speed of the operation. Most UAV multispectral images have multiple bands, and in every band, ground targets have a variety of reflection characteristics that will result in diverse feature quality for feature matching. In this research, an information entropy-based evaluation method is used to select the optimal band for feature matching among the UAV images. To produce more robust matching results for the following alignment step, the evaluation method takes the contrast and spatial distribution of the feature points into consideration at the same time. In most common image mosaicking processes, the digital orthophoto map (DOM) is generated to achieve maximum spatial accuracy. During this process, the original image data will experience considerable irregular resampling, and the process is also unstable in some circumstances. The alignment step uses a simplified projection model that treats the ground as planar is provided, by which the alignment parameters are applied directly to the images instead of generating 3D points, to avoid irregular resampling and unstable 3D reconstruction. The proposed method is proved to be more efficient and accurate and has lower spectral distortion than state-of-the-art mosaicking software.

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“…Suomalainen et al [19] compensated for the inferior quality of direct geo-referencing information through simultaneous integration of captured frame images and a Digital Elevation Model (DEM), which was derived from the frame images. Ramirez-Paredes et al [20] Remote Sens. 2016, 8,796 3 of 22 presented a computer-vision approach for the indirect geo-referencing of the hyperspectral scenes, which estimated a set of transformation parameters relating the reference frames of frame and hyperspectral imagery.…”

Section: Introductionmentioning

confidence: 99%

Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery

et al. 2016

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Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.

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Low‐altitude Terrestrial Spectroscopy from a Pushbroom Sensor

Cited by 14 publications

References 39 publications

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

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

A Simplified Method for UAV Multispectral Images Mosaicking

Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery

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