The Experts below are selected from a list of 3399 Experts worldwide ranked by ideXlab platform
Patrick Hostert - One of the best experts on this subject based on the ideXlab platform.
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Ensemble Learning From Synthetically Mixed Training Data for Quantifying Urban Land Cover With Support Vector Regression
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Stefan Suess, Patrick HostertAbstract:Generating synthetically mixed data from library spectra provides a direct means to train empirical regression models for subpixel mapping. In order to best represent the subpixel composition of image data, the generation of synthetic mixtures must incorporate a multitude of mixing possibilities. This can lead to an excessive amount of training samples. We show that increasing mixing complexity in the training set improves model performance when quantifying Urban Land Cover with support vector regression (SVR). To cope with the challenging increase in the number of training samples, we propose the use of ensemble learning based on bootstrap aggregation from synthetically mixed training data. The workflow is tested on simulated spaceborne imaging spectrometer data acquired over Berlin, Germany. Comparisons to SVR without bagging and multiple endmember spectral mixture analysis reveal the usefulness of the methodology for quantitative Urban mapping.
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A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover
Remote Sensing, 2014Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Benjamin Jakimow, Andreas Rabe, Jochem Verrelst, Patrick HostertAbstract:Quantitative methods for mapping sub-pixel Land Cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying Urban Land Cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex Urban surface types, i.e., rooftops, pavements, grass- and tree-Covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN) or limited (RFR and PLSR) performances when comprehensively mapping Urban Land Cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying Urban Land Cover from imaging spectrometer data at multiple scales.
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support vector regression and synthetically mixed training data for quantifying Urban Land Cover
Remote Sensing of Environment, 2013Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Laurent Tits, Ben Somers, Patrick HostertAbstract:Abstract Exploiting imaging spectrometer data with machine learning algorithms has been demonstrated to be an excellent choice for mapping ecologically meaningful Land Cover categories in spectrally complex Urban environments. However, the potential of kernel-based regression techniques for quantitatively analyzing Urban composition has not yet been fully explored. To a great extent, this can be explained by difficulties in deriving quantitative training information that reliably represents pairs of spectral signatures with associated Land Cover fractions needed for empirical modeling. In this paper we present an approach to circumvent this limitation by combining support vector regression (SVR) with synthetically mixed training data to map sub-pixel fractions of single Urban Land Cover categories of interest. This approach was tested on Hyperspectral Mapper (HyMap) data acquired over Berlin, Germany. Fraction estimates were validated with extensive manual mappings and compared to fractions derived from multiple endmember spectral mixture analysis (MESMA). Our regression results demonstrate that the sets of multiple mixtures yielded high accuracies for quantitative estimates for four spectrally complex Urban Land Cover types, i.e., fractions of impervious rooftops and pavements, as well as grass- and tree-Covered areas. Despite the extrapolation uncertainty of SVR, which resulted in fraction values below 0% and above 100%, physically meaningful model outputs were reported for a clear majority of pixels, and visual inspection underpinned the quality of produced fraction maps. Statistical accuracy assessment with detailed reference information for 92 Urban blocks showed linear relations with R 2 values of 0.86, 0.58, 0.81 and 0.85 for the four categories, respectively. Mean absolute errors (MAE) ranged from 6.4 to 12.8% and block-wise sums of the four individually modeled category fractions were always around 100%. Results of MESMA followed similar trends, but with slightly lower accuracies. Our findings demonstrate that the combination of SVR and synthetically mixed training data enable the use of empirical regression for sub-pixel mapping. Thus, the strengths of kernel-based approaches for quantifying Urban Land Cover from imaging spectrometer data can be well utilized. Remaining uncertainties and limitations were related to the known phenomena of spectral similarity or ambiguity of Urban materials, the spectral deficiencies in shaded areas, or the dependency on comprehensive and representative spectral libraries. Therefore, the suggested workflow constitutes a new flexible and extendable universal modeling approach to map Land Cover fractions.
Akpona Okujeni - One of the best experts on this subject based on the ideXlab platform.
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Ensemble Learning From Synthetically Mixed Training Data for Quantifying Urban Land Cover With Support Vector Regression
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Stefan Suess, Patrick HostertAbstract:Generating synthetically mixed data from library spectra provides a direct means to train empirical regression models for subpixel mapping. In order to best represent the subpixel composition of image data, the generation of synthetic mixtures must incorporate a multitude of mixing possibilities. This can lead to an excessive amount of training samples. We show that increasing mixing complexity in the training set improves model performance when quantifying Urban Land Cover with support vector regression (SVR). To cope with the challenging increase in the number of training samples, we propose the use of ensemble learning based on bootstrap aggregation from synthetically mixed training data. The workflow is tested on simulated spaceborne imaging spectrometer data acquired over Berlin, Germany. Comparisons to SVR without bagging and multiple endmember spectral mixture analysis reveal the usefulness of the methodology for quantitative Urban mapping.
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A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover
Remote Sensing, 2014Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Benjamin Jakimow, Andreas Rabe, Jochem Verrelst, Patrick HostertAbstract:Quantitative methods for mapping sub-pixel Land Cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying Urban Land Cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex Urban surface types, i.e., rooftops, pavements, grass- and tree-Covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN) or limited (RFR and PLSR) performances when comprehensively mapping Urban Land Cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying Urban Land Cover from imaging spectrometer data at multiple scales.
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support vector regression and synthetically mixed training data for quantifying Urban Land Cover
Remote Sensing of Environment, 2013Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Laurent Tits, Ben Somers, Patrick HostertAbstract:Abstract Exploiting imaging spectrometer data with machine learning algorithms has been demonstrated to be an excellent choice for mapping ecologically meaningful Land Cover categories in spectrally complex Urban environments. However, the potential of kernel-based regression techniques for quantitatively analyzing Urban composition has not yet been fully explored. To a great extent, this can be explained by difficulties in deriving quantitative training information that reliably represents pairs of spectral signatures with associated Land Cover fractions needed for empirical modeling. In this paper we present an approach to circumvent this limitation by combining support vector regression (SVR) with synthetically mixed training data to map sub-pixel fractions of single Urban Land Cover categories of interest. This approach was tested on Hyperspectral Mapper (HyMap) data acquired over Berlin, Germany. Fraction estimates were validated with extensive manual mappings and compared to fractions derived from multiple endmember spectral mixture analysis (MESMA). Our regression results demonstrate that the sets of multiple mixtures yielded high accuracies for quantitative estimates for four spectrally complex Urban Land Cover types, i.e., fractions of impervious rooftops and pavements, as well as grass- and tree-Covered areas. Despite the extrapolation uncertainty of SVR, which resulted in fraction values below 0% and above 100%, physically meaningful model outputs were reported for a clear majority of pixels, and visual inspection underpinned the quality of produced fraction maps. Statistical accuracy assessment with detailed reference information for 92 Urban blocks showed linear relations with R 2 values of 0.86, 0.58, 0.81 and 0.85 for the four categories, respectively. Mean absolute errors (MAE) ranged from 6.4 to 12.8% and block-wise sums of the four individually modeled category fractions were always around 100%. Results of MESMA followed similar trends, but with slightly lower accuracies. Our findings demonstrate that the combination of SVR and synthetically mixed training data enable the use of empirical regression for sub-pixel mapping. Thus, the strengths of kernel-based approaches for quantifying Urban Land Cover from imaging spectrometer data can be well utilized. Remaining uncertainties and limitations were related to the known phenomena of spectral similarity or ambiguity of Urban materials, the spectral deficiencies in shaded areas, or the dependency on comprehensive and representative spectral libraries. Therefore, the suggested workflow constitutes a new flexible and extendable universal modeling approach to map Land Cover fractions.
Sebastian Van Der Linden - One of the best experts on this subject based on the ideXlab platform.
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Ensemble Learning From Synthetically Mixed Training Data for Quantifying Urban Land Cover With Support Vector Regression
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Stefan Suess, Patrick HostertAbstract:Generating synthetically mixed data from library spectra provides a direct means to train empirical regression models for subpixel mapping. In order to best represent the subpixel composition of image data, the generation of synthetic mixtures must incorporate a multitude of mixing possibilities. This can lead to an excessive amount of training samples. We show that increasing mixing complexity in the training set improves model performance when quantifying Urban Land Cover with support vector regression (SVR). To cope with the challenging increase in the number of training samples, we propose the use of ensemble learning based on bootstrap aggregation from synthetically mixed training data. The workflow is tested on simulated spaceborne imaging spectrometer data acquired over Berlin, Germany. Comparisons to SVR without bagging and multiple endmember spectral mixture analysis reveal the usefulness of the methodology for quantitative Urban mapping.
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A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover
Remote Sensing, 2014Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Benjamin Jakimow, Andreas Rabe, Jochem Verrelst, Patrick HostertAbstract:Quantitative methods for mapping sub-pixel Land Cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying Urban Land Cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex Urban surface types, i.e., rooftops, pavements, grass- and tree-Covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN) or limited (RFR and PLSR) performances when comprehensively mapping Urban Land Cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying Urban Land Cover from imaging spectrometer data at multiple scales.
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support vector regression and synthetically mixed training data for quantifying Urban Land Cover
Remote Sensing of Environment, 2013Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Laurent Tits, Ben Somers, Patrick HostertAbstract:Abstract Exploiting imaging spectrometer data with machine learning algorithms has been demonstrated to be an excellent choice for mapping ecologically meaningful Land Cover categories in spectrally complex Urban environments. However, the potential of kernel-based regression techniques for quantitatively analyzing Urban composition has not yet been fully explored. To a great extent, this can be explained by difficulties in deriving quantitative training information that reliably represents pairs of spectral signatures with associated Land Cover fractions needed for empirical modeling. In this paper we present an approach to circumvent this limitation by combining support vector regression (SVR) with synthetically mixed training data to map sub-pixel fractions of single Urban Land Cover categories of interest. This approach was tested on Hyperspectral Mapper (HyMap) data acquired over Berlin, Germany. Fraction estimates were validated with extensive manual mappings and compared to fractions derived from multiple endmember spectral mixture analysis (MESMA). Our regression results demonstrate that the sets of multiple mixtures yielded high accuracies for quantitative estimates for four spectrally complex Urban Land Cover types, i.e., fractions of impervious rooftops and pavements, as well as grass- and tree-Covered areas. Despite the extrapolation uncertainty of SVR, which resulted in fraction values below 0% and above 100%, physically meaningful model outputs were reported for a clear majority of pixels, and visual inspection underpinned the quality of produced fraction maps. Statistical accuracy assessment with detailed reference information for 92 Urban blocks showed linear relations with R 2 values of 0.86, 0.58, 0.81 and 0.85 for the four categories, respectively. Mean absolute errors (MAE) ranged from 6.4 to 12.8% and block-wise sums of the four individually modeled category fractions were always around 100%. Results of MESMA followed similar trends, but with slightly lower accuracies. Our findings demonstrate that the combination of SVR and synthetically mixed training data enable the use of empirical regression for sub-pixel mapping. Thus, the strengths of kernel-based approaches for quantifying Urban Land Cover from imaging spectrometer data can be well utilized. Remaining uncertainties and limitations were related to the known phenomena of spectral similarity or ambiguity of Urban materials, the spectral deficiencies in shaded areas, or the dependency on comprehensive and representative spectral libraries. Therefore, the suggested workflow constitutes a new flexible and extendable universal modeling approach to map Land Cover fractions.
Michael Schmitt - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - Fusing Multi-Seasonal Sentinel-2 Images with Residual Convolutional Neural Networks for Local Climate Zone-Derived Urban Land Cover Classification
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Michael SchmittAbstract:This paper proposes a framework to fuse multi-seasonal Sentinel-2 images, with application on LCZ-derived Urban Land Cover classification. Cross-validation over a seven-city study area in central Europe demonstrates its consistently better performance over several previous approaches, with the same experimental setup. Based on our previous work, we can conclude that decision-level fusion is better than feature-level fusion for similar tasks at similar scale with multi-seasonal Sentinel-2 images. With the framework, Urban Land Cover maps of several cities are produced. The visualization of two exemplary areas shows Urban structures that are consistent with existing datasets. This framework can be also generally beneficial for other types of Urban mapping.
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Fusing Multi-Seasonal Sentinel-2 Images with Residual Convolutional Neural Networks for Local Climate Zone-Derived Urban Land Cover Classification
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Michael SchmittAbstract:This paper proposes a framework to fuse multi-seasonal Sentinel-2 images, with application on LCZ-derived Urban Land Cover classification. Cross-validation over a seven-city study area in central Europe demonstrates its consistently better performance over several previous approaches, with the same experimental setup. Based on our previous work, we can conclude that decision-level fusion is better than feature-level fusion for similar tasks at similar scale with multi-seasonal Sentinel-2 images. With the framework, Urban Land Cover maps of several cities are produced. The visualization of two exemplary areas shows Urban structures that are consistent with existing datasets. This framework can be also generally beneficial for other types of Urban mapping.
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Fusing Multiseasonal Sentinel-2 Imagery for Urban Land Cover Classification With Multibranch Residual Convolutional Neural Networks
IEEE Geoscience and Remote Sensing Letters, 1Co-Authors: Michael SchmittAbstract:Exploiting multitemporal Sentinel-2 images for Urban Land Cover classification has become an important research topic, since these images have become globally available at relatively fine temporal resolution, thus offering great potential for large-scale Land Cover mapping. However, appropriate exploitation of the images needs to address problems such as cloud Cover inherent to optical satellite imagery. To this end, we propose a simple yet effective decision-level fusion approach for Urban Land Cover prediction from multiseasonal Sentinel-2 images, using the state-of-the-art residual convolutional neural networks (ResNet). We extensively tested the approach in a cross-validation manner over a seven-city study area in central Europe. Both quantitative and qualitative results demonstrated the superior performance of the proposed fusion approach over several baseline approaches, including observation- and feature-level fusion.
Stefan Suess - One of the best experts on this subject based on the ideXlab platform.
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Ensemble Learning From Synthetically Mixed Training Data for Quantifying Urban Land Cover With Support Vector Regression
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Akpona Okujeni, Sebastian Van Der Linden, Stefan Suess, Patrick HostertAbstract:Generating synthetically mixed data from library spectra provides a direct means to train empirical regression models for subpixel mapping. In order to best represent the subpixel composition of image data, the generation of synthetic mixtures must incorporate a multitude of mixing possibilities. This can lead to an excessive amount of training samples. We show that increasing mixing complexity in the training set improves model performance when quantifying Urban Land Cover with support vector regression (SVR). To cope with the challenging increase in the number of training samples, we propose the use of ensemble learning based on bootstrap aggregation from synthetically mixed training data. The workflow is tested on simulated spaceborne imaging spectrometer data acquired over Berlin, Germany. Comparisons to SVR without bagging and multiple endmember spectral mixture analysis reveal the usefulness of the methodology for quantitative Urban mapping.
