Geometric correction of APEX hyperspectral data

Vreys, Kristin; Iordache, Marian-Daniel; Biesemans, Jan; Meuleman, Koen

doi:10.1515/mgrsd-2016-0006

Cited by 23 publications

(22 citation statements)

References 4 publications

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“…The boresight angles account for misalignments between the IMU axis and the sensor axis and they are computed every time APEX is mounted in the aircraft. Ground control points (GCPs) selected from orthophotos are identified in the APEX data and, following a Monte Carlo procedure, optimal parameters to compensate for roll, pitch and yaw errors are inferred, greatly reducing geolocation errors which are usually lower than 1 unbinned spatial pixel (Vreys et al, 2016b). The orthorectification and georeferencing module receives the sensor interior and exterior orientation, boresight calibration data and digital elevation model (DEM) data as input.…”

Section: Spectra Geolocation and Georeferencingmentioning

confidence: 99%

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High-resolution mapping of the NO<sub>2</sub> spatial distribution over Belgian urban areas based on airborne APEX remote sensing

et al. 2017

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Abstract. We present retrieval results of tropospheric nitrogen dioxide (NO 2 ) vertical column densities (VCDs), mapped at high spatial resolution over three Belgian cities, based on the DOAS analysis of Airborne Prism EXperiment (APEX) observations. APEX, developed by a Swiss-Belgian consortium on behalf of ESA (European Space Agency), is a pushbroom hyperspectral imager characterised by a high spatial resolution and high spectral performance. APEX data have been acquired under clear-sky conditions over the two largest and most heavily polluted Belgian cities, i.e. Antwerp and Brussels on 15 April and 30 June 2015. Additionally, a number of background sites have been covered for the reference spectra. The APEX instrument was mounted in a Dornier DO-228 aeroplane, operated by Deutsches Zentrum für Luft-und Raumfahrt (DLR). NO 2 VCDs were retrieved from spatially aggregated radiance spectra allowing urban plumes to be resolved at the resolution of 60 × 80 m 2 . The main sources in the Antwerp area appear to be related to the (petro)chemical industry while traffic-related emissions dominate in Brussels. The NO 2 levels observed in Antwerp range between 3 and 35 × 10 15 molec cm −2 , with a mean VCD of 17.4 ± 3.7 × 10 15 molec cm −2 . In the Brussels area, smaller levels are found, ranging between 1 and 20 × 10 15 molec cm −2 and a mean VCD of 7.7 ± 2.1 × 10 15 molec cm −2 . The overall errors on the retrieved NO 2 VCDs are on average 21 and 28 % for the Antwerp and Brussels data sets. Low VCD retrievals are mainly limited by noise (1σ slant error), while high retrievals are mainly limited by systematic errors. Compared to coincident car mobile-DOAS measurements taken in Antwerp and Brussels, both data sets are in good agreement with correlation coefficients around 0.85 and slopes close to unity. APEX retrievals tend to be, on average, 12 and 6 % higher for Antwerp and Brussels, respectively. Results demonstrate that the NO 2 distribution in an urban environment, and its finescale variability, can be mapped accurately with high spatial resolution and in a relatively short time frame, and the contributing emission sources can be resolved. High-resolution quantitative information about the atmospheric NO 2 horizontal variability is currently rare, but can be very valuable for (air quality) studies at the urban scale.

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Section: Spectra Geolocation and Georeferencingmentioning

confidence: 99%

“…The module outputs the position and the complete viewing geometry for each pixel or measured spectrum, allowing a proper mapping of the retrieved NO 2 spatial distribution. In Vreys et al (2016b), the georeferencing module and its qualitative and quantitative assessment is discussed more deeply.…”

Section: Spectra Geolocation and Georeferencingmentioning

confidence: 99%

High-resolution mapping of the NO<sub>2</sub> spatial distribution over Belgian urban areas based on airborne APEX remote sensing

et al. 2017

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“…In the case of APEX data it was the VITO company (Flemish Institute for Technological Research), the procedure of data pre-processing is described in detail by Schaepman et al [2015], Sterckx et al [2016] and Vreys et al [2016a;2016b]. In the case of AISA Dual it was Geodis Brno; the preprocessing methods are summed up in the internal technical report [AISA, 2013] and include in-flight calibration data, field spectrometric measurements using ASD FieldSpec 3, and digital elevation model.…”

Section: Image Data Pre-processingmentioning

confidence: 99%

Classification of Tundra Vegetation in the Krkonoše Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

Kupková

Červená

Suchá

et al. 2017

European Journal of Remote Sensing

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The aim of this study was to evaluate and compare suitability of aerial hyperspectral data (AISA Dual and APEX sensors) and Sentinel-2A data for classification of tundra vegetation cover in the Krkonoše Mts. National Park. We compared classification results (accuracy, maps) of pixel-based (Maximum Likelihood, Suport Vector Machine and Neural Net) and objectbased approaches. The best classification results (overall accuracy 84.3%, Kappa coefficient = 0.81) were achieved for AISA Dual data using per-pixel SVM classifier for 40 PCA bands. The best classification results of APEX though were only 1.7 percentage points lower. To get comparable results for Sentinel-2A classification legend had to be simplified. With the simplified legend the accuracy using MLC classifier reached 77.7%.

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“…Image pre-processing was done by an automated processing chain at the Flemish Institute for Technological Research [40]. This process consists of geometric correction (using direct georeferencing [41]), projection in the Belgian Lambert 72 coordinate system and atmospheric correction using a MODTRAN4 radiative transfer model [42,43].…”

Section: Spectral Librarymentioning

confidence: 99%

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

et al. 2017

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Spectral unmixing of urban land cover relies on representative endmember libraries. For repeated mapping of multiple cities, the use of a generic spectral library, capturing the vast spectral variability of urban areas, would constitute a more operational alternative to the tedious development of image-specific libraries prior to mapping. The size and heterogeneity of such a generic library requires an efficient pruning technique to extract site-specific spectral libraries. We propose the "Automated MUsic and spectral Separability based Endmember Selection technique" (AMUSES), which selects endmember subsets with respect to the image to be processed, while accounting for internal redundancy. Experiments on simulated hyperspectral data from Brussels (Belgium) showed that AMUSES selects more relevant endmembers compared to the conventional Iterative Endmember Selection (IES) approach. This ultimately improved mapping results (kappa increased from 0.71 to 0.83). Experiments on real HyMap data from Berlin (Germany) using a combination of libraries from different cities underlined the potential of AMUSES for handling libraries with increasing levels of generality (RMSE decreased from 0.18 to 0.15, while only using 55% of the number of spectra compared to IES). Our findings contribute to the value of generic spectral databases in the development of efficient urban mapping workflows.

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Geometric correction of APEX hyperspectral data

Cited by 23 publications

References 4 publications

High-resolution mapping of the NO<sub>2</sub> spatial distribution over Belgian urban areas based on airborne APEX remote sensing

High-resolution mapping of the NO<sub>2</sub> spatial distribution over Belgian urban areas based on airborne APEX remote sensing

Classification of Tundra Vegetation in the Krkonoše Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

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Geometric correction of APEX hyperspectral data

Cited by 23 publications

References 4 publications

High-resolution mapping of the NO&lt;sub&gt;2&lt;/sub&gt; spatial distribution over Belgian urban areas based on airborne APEX remote sensing

High-resolution mapping of the NO&lt;sub&gt;2&lt;/sub&gt; spatial distribution over Belgian urban areas based on airborne APEX remote sensing

Classification of Tundra Vegetation in the Krkonoše Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

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High-resolution mapping of the NO<sub>2</sub> spatial distribution over Belgian urban areas based on airborne APEX remote sensing

High-resolution mapping of the NO<sub>2</sub> spatial distribution over Belgian urban areas based on airborne APEX remote sensing