Atmospheric correction of APEX hyperspectral data

Sterckx, Sindy; Vreys, Kristin; Biesemans, Jan; Iordache, Marian-Daniel; Bertels, Luc; Meuleman, Koen

doi:10.1515/mgrsd-2015-0022

Cited by 23 publications

(37 citation statements)

References 16 publications

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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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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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“…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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“…The instrument was configured to perform measurements over 288 spectral bands but bands 147-160 and 198-219 were removed from further analyses due to atmospheric absorption effects, leaving 252 bands. The initial level 1C product was geometrically corrected using direct georeferencing and atmospherically corrected using the MODTRAN4 radiative transfer code to derive bottom-of atmosphere reflectance [41][42][43][44]. The final level 2B image was registered in the Belgian Lambert 72 coordinate system with dimensions encompassing 8860 rows by 3491 columns and a pixel size of 2 × 2 m 2 .…”

Section: Apex Hyperspectral Imagery and Image Preprocessingmentioning

confidence: 99%

Synergistic Use of LiDAR and APEX Hyperspectral Data for High-Resolution Urban Land Cover Mapping

Priem

Canters

2016

Remote Sensing

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Land cover mapping of the urban environment by means of remote sensing remains a distinct challenge due to the strong spectral heterogeneity and geometric complexity of urban scenes. Airborne imaging spectroscopy and laser altimetry have each made remarkable contributions to urban mapping but synergistic use of these relatively recent data sources in an urban context is still largely underexplored. In this study a synergistic workflow is presented to cope with the strong diversity of materials in urban areas, as well as with the presence of shadow. A high-resolution APEX hyperspectral image and a discrete waveform LiDAR dataset covering the Eastern part of Brussels were made available for this research. Firstly, a novel shadow detection method based on LiDAR intensity-APEX brightness thresholding is proposed and compared to commonly used approaches for shadow detection. A combination of intensity-brightness thresholding with DSM model-based shadow detection is shown to be an efficient approach for shadow mask delineation. To deal with spectral similarity of different types of urban materials and spectral distortion induced by shadow cover, supervised classification of shaded and sunlit areas is combined with iterative LiDAR post-classification correction. Results indicate that height, slope and roughness features contribute to improved classification accuracies in descending order of importance. Results of this study illustrate the potential of synergistic application of hyperspectral imagery and LiDAR for urban land cover mapping.

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Atmospheric correction of APEX hyperspectral data

Cited by 23 publications

References 16 publications

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

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

Synergistic Use of LiDAR and APEX Hyperspectral Data for High-Resolution Urban Land Cover Mapping

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