F. Rottensteiner - Academia.edu (original) (raw)

Conference Presentations by F. Rottensteiner

Research paper thumbnail of MULTI-TASK DEEP LEARNING WITH INCOMPLETE TRAINING SAMPLES FOR THE IMAGE-BASED PREDICTION OF VARIABLES DESCRIBING SILK FABRICS

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W6, 2019

This paper presents a method for the classification of images of silk fabrics with the aim to pre... more This paper presents a method for the classification of images of silk fabrics with the aim to predict properties such as the place and time of origin and the production technique. The proposed method was developed in the context of the EU project SILKNOW (http://silknow.eu/). In the context of classification, we address the problem of limited as well as not fully labelled data and investigate the connection between the distinct variables. A pre-trained Convolutional Neural Network (CNN) is used for the feature extraction and a classification network realizing Multi-task learning (MTL) is trained based on these features. The training procedure is adapted to enable the consideration of images that do not have a label for all tasks. Additionally, MTL with fully labeled training data is investigated for the classification of silk fabrics. The impact of both MTL approaches is compared to single-task learning based on two different class structures. We achieve overall accuracies of 92-95% and average F1-scores of 88-90% in our best experiments.

Papers by F. Rottensteiner

Research paper thumbnail of Appearance based deep domain adaptation for the classification of aerial images

ISPRS Journal of Photogrammetry and Remote Sensing

Research paper thumbnail of A New Approach for an Incremental Orientation of Micro-Uav Image Sequences

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Civil applications for small size unmanned aerial vehicles (UAV) have become quite important in r... more Civil applications for small size unmanned aerial vehicles (UAV) have become quite important in recent years and so have accurate orientation and navigation of these devices in unknown terrain. In this work we focus on on-line compatible positioning in facade observation based on monocular low resolution still images acquired by a camera mounted on a UAV. Also, a 3D point cloud of the facade is generated. This allows further processing steps, e. g. navigation assistance, collision avoidance or the evaluation of the point cloud density, verifying completeness of the data. To be able to deal with the increasing amount of observations and unknown parameters we implement an incremental bundle adjustment based on automatically determined tie points and sliding image triplets. The tripletwise orientation allows for an efficient double cross-check of the detected feature points and hence guarantees reliable initial values for the nonlinear bundle adjustment. The initial values are estimated within a convex formulation delivering a sound basis for the incremental adjustment. Our algorithm is evaluated by means of imagery we took of the facade of the Welfenschloss in Hannover, captured from a manually flown Microdrones md4-200 micro-UAV. We compare the orientation results of our approach with an approach in which initial values for the unknown object coordinates are computed algebraically. * Corresponding author we adaptively remove already oriented images from incremental adjustment based on the number of supporting object points measured in the related image to keep computation time constant.

Research paper thumbnail of Boosted Unsupervised Multi-Source Selection for Domain Adaptation

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences

Supervised machine learning needs high quality, densely sampled and labelled training data. Trans... more Supervised machine learning needs high quality, densely sampled and labelled training data. Transfer learning (TL) techniques have been devised to reduce this dependency by adapting classifiers trained on different, but related, (source) training data to new (target) data sets. A problem in TL is how to quantify the relatedness of a source quickly and robustly, because transferring knowledge from unrelated data can degrade the performance of a classifier. In this paper, we propose a method that can select a nearly optimal source from a large number of candidate sources. This operation depends only on the marginal probability distributions of the data, thus allowing the use of the often abundant unlabelled data. We extend this method to multi-source selection by optimizing a weighted combination of sources. The source weights are computed using a very fast boosting-like optimization scheme. The run-time complexity of our method scales linearly in regard to the number of candidate sou...

Research paper thumbnail of Conditional Random Fields for the Classification of Lidar Point Clouds

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

In this paper we propose a probabilistic supervised classification algorithm for LiDAR (Light Det... more In this paper we propose a probabilistic supervised classification algorithm for LiDAR (Light Detection And Ranging) point clouds. Several object classes (i.e. ground, building and vegetation) can be separated reliably by considering each point's neighbourhood. Based on Conditional Random Fields (CRF) this contextual information can be incorporated into classification process in order to improve results. Since we want to perform a point-wise classification, no primarily segmentation is needed. Therefore, each 3D point is regarded as a graph's node, whereas edges represent links to the nearest neighbours. Both nodes and edges are associated with features and have effect on the classification. We use some features available from full waveform technology such as amplitude, echo width and number of echoes as well as some extracted geometrical features. The aim of the paper is to describe the CRF model setup for irregular point clouds, present the features used for classification, and to discuss some results. The resulting overall accuracy is about 94 %.

Research paper thumbnail of Accurate Matching and Reconstruction of Line Features from Ultra High Resolution Stereo Aerial Images

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

In this study, a new reconstruction approach is proposed for the line segments that are nearly-al... more In this study, a new reconstruction approach is proposed for the line segments that are nearly-aligned (≤ 10º) with the epipolar line. The method manipulates the redundancy inherent in line pair-relations to generate artificial 3D point entities and utilize those entities during the estimation process to improve the height values of the reconstructed line segments. The best point entities for the reconstruction are selected based on a newly proposed weight function. To test the performance of the proposed approach, we selected three test patches over a built up area of the city of Vaihingen-Germany. Based on the results, the proposed approach produced highly promising reconstruction results for the line segments that are nearly-aligned with the epipolar line.

Research paper thumbnail of Rooftop Rainwater Harvesting for Mombasa: Scenario Development with Image Classification and Water Resources Simulation

Water

Mombasa faces severe water scarcity problems. The existing supply is unable to satisfy the demand... more Mombasa faces severe water scarcity problems. The existing supply is unable to satisfy the demand. This article demonstrates the combination of satellite image analysis and modelling as tools for the development of an urban rainwater harvesting policy. For developing a sustainable remedy policy, rooftop rainwater harvesting (RRWH) strategies were implemented into the water supply and demand model WEAP (Water Evaluation and Planning System). Roof areas were detected using supervised image classification. Future population growth, improved living standards, and climate change predictions until 2035 were combined with four management strategies. Image classification techniques were able to detect roof areas with acceptable accuracy. The simulated annual yield of RRWH ranged from 2.3 to 23 million cubic meters (MCM) depending on the extent of the roof area. Apart from potential RRWH, additional sources of water are required for full demand coverage.

Research paper thumbnail of Network Detection in Raster Data Using Marked Point Processes

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

We propose a new approach for the automatic detection of network structures in raster data. The m... more We propose a new approach for the automatic detection of network structures in raster data. The model for the network structure is represented by a graph whose nodes and edges correspond to junction-points and to connecting line segments, respectively; nodes and edges are further described by certain parameters. We embed this model in the probabilistic framework of marked point processes and determine the most probable configuration of objects by stochastic sampling. That is, different graph configurations are constructed randomly by modifying the graph entity parameters, by adding and removing nodes and edges to/ from the current graph configuration. Each configuration is then evaluated based on the probabilities of the changes and an energy function describing the conformity with a predefined model. By using the Reversible Jump Markov Chain Monte Carlo sampler, a global optimum of the energy function is determined. We apply our method to the detection of river and tidal channel ne...

Research paper thumbnail of Contextual Classification of Point Cloud Data by Exploiting Individual 3D Neigbourhoods

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote ... more The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on…

Research paper thumbnail of Report: Object Extraction for 3D City Models, Road Databases, and Traffic Monitoring - Concepts, Algorithms, and Evaluation

Research paper thumbnail of CMRT09 Object Extraction for 3D City Models, Road Databases, and Traffic Monitoring - Concepts, Algorithms, and Evaluation

and Remote Sensing. Reproduction of this volume or any parts thereof (excluding short quotations ... more and Remote Sensing. Reproduction of this volume or any parts thereof (excluding short quotations for the use in the preparation of reviews and technical and scientific papers) may be made only after obtaining the specific approval of the publisher. The papers appearing in this volume reflect the authors' opinions. Their inclusion in this publication does not necessarily constitute endorsement by the editors or by the publisher. Authors retain all rights to individual papers.

Research paper thumbnail of Probabilistic Multi-Person Tracking Using Dynamic Bayes Networks

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015

Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on... more Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicl...

Research paper thumbnail of Transfer Learning Based on Logistic Regression

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015

In this paper we address the problem of classification of remote sensing images in the framework ... more In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer learning in remote sensing on the basis of logistic regression. Logistic regression is a discriminative probabilistic classifier of low computational complexity, which can deal with multiclass problems. This research area deals with methods that solve problems in which labelled training data sets are assumed to be available only for a source domain, while classification is needed in the target domain with different, yet related characteristics. Classification takes place with a model of weight coefficients for hyperplanes which separate features in the transformed feature space. In term of logistic regression, our domain adaptation method adjusts the model parameters by iterative labelling of the target test data set. These labelled data features are iteratively added...

Research paper thumbnail of Semi-automatic verification of cropland and grassland using very high resolution mono-temporal satellite images

ISPRS Journal of Photogrammetry and Remote Sensing, 2014

ABSTRACT Many public and private decisions rely on geospatial information stored in a GIS databas... more ABSTRACT Many public and private decisions rely on geospatial information stored in a GIS database. For good decision making this information has to be complete, consistent, accurate and up-to-date. In this paper we introduce a new approach for the semi-automatic verification of a specific part of the, possibly outdated GIS database, namely cropland and grassland objects, using mono-temporal very high resolution (VHR) multispectral satellite images. The approach consists of two steps: first, a supervised pixel-based classification based on a Markov Random Field is employed to extract image regions which contain agricultural areas (without distinction between cropland and grassland), and these regions are intersected with boundaries of the agricultural objects from the GIS database. Subsequently, GIS objects labelled as cropland or grassland in the database and showing agricultural areas in the image are subdivided into different homogeneous regions by means of image segmentation, followed by a classification of these segments into either cropland or grassland using a Support Vector Machine. The classification result of all segments belonging to one GIS object are finally merged and compared with the GIS database label. The developed approach was tested on a number of images. The evaluation shows that errors in the GIS database can be significantly reduced while also speeding up the whole verification task when compared to a manual process.

Research paper thumbnail of Classification of urban LiDAR data using conditional random field and random forests

Joint Urban Remote Sensing Event 2013, 2013

ABSTRACT In this work we address the task of contextual classification of an airborne LiDAR point... more ABSTRACT In this work we address the task of contextual classification of an airborne LiDAR point cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random Field (CRF) framework. A CRF has been shown to deliver good results discerning multiple classes. It is a flexible approach for obtaining a reliable classification even in complex urban scenes. The incorporation of multi-scale features improves the results further. Based on the classification results, 2D building and tree objects are generated and evaluated by the benchmark of ISPRS WG III/4.

Research paper thumbnail of Advanced methods for automated object extraction from LiDAR in urban areas

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012

This paper gives an overview about advanced techniques for classification and object detection th... more This paper gives an overview about advanced techniques for classification and object detection that are being adopted for urban object detection from LiDAR data. The paper covers local supervised classifiers such as AdaBoost, SVM and Random Forests, statistical models of context such as Markov Random Fields and Conditional Random Fields, and sampling techniques. The relevance of features is also discussed. Applications include DTM generation and the extraction of buildings, trees, and low vegetation.

Research paper thumbnail of Classification of multitemporal remote sensing data using Conditional Random Fields

2010 IAPR Workshop on Pattern Recognition in Remote Sensing, 2010

Research paper thumbnail of Articles in Press–available online

Research paper thumbnail of A Dynamic Bayes Network for visual Pedestrian Tracking

Many tracking systems rely on independent single frame detections that are handled as observation... more Many tracking systems rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach suggests a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, prior scene information, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forests-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available dataset captured in a challenging outdoor scenario. Using the adaptive classifier, our system is able to keep track of pedestrians over long distances while at the same time supporting the localisation of the people. The results show that the derived trajectories achieve a geometric accuracy superior to the one achieved by modelling the image positions as observations.

Research paper thumbnail of Extraction of fluvial networks in lidar data using marked point processes

We propose a method for the automatic extraction of fluvial networks in lidar data with the aim t... more We propose a method for the automatic extraction of fluvial networks in lidar data with the aim to obtain a connected network represented by the fluvial channels' skeleton. For that purpose we develop a two-step approach. First, we fit rectangles to the data using a stochastic optimization based on a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler and simulated annealing. High gradients on the rectangles' border and non-overlapping areas of the objects are introduced as model in the optimization process. In a second step, we determine the principal axes of the rectangles and their intersection points. Based on this a network graph is constructed in which nodes represent junction points or end points, respectively, and edges in-between straight line segments. We evaluate our method on lidar data with a tidal channel network and show some preliminary results.

Research paper thumbnail of MULTI-TASK DEEP LEARNING WITH INCOMPLETE TRAINING SAMPLES FOR THE IMAGE-BASED PREDICTION OF VARIABLES DESCRIBING SILK FABRICS

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W6, 2019

This paper presents a method for the classification of images of silk fabrics with the aim to pre... more This paper presents a method for the classification of images of silk fabrics with the aim to predict properties such as the place and time of origin and the production technique. The proposed method was developed in the context of the EU project SILKNOW (http://silknow.eu/). In the context of classification, we address the problem of limited as well as not fully labelled data and investigate the connection between the distinct variables. A pre-trained Convolutional Neural Network (CNN) is used for the feature extraction and a classification network realizing Multi-task learning (MTL) is trained based on these features. The training procedure is adapted to enable the consideration of images that do not have a label for all tasks. Additionally, MTL with fully labeled training data is investigated for the classification of silk fabrics. The impact of both MTL approaches is compared to single-task learning based on two different class structures. We achieve overall accuracies of 92-95% and average F1-scores of 88-90% in our best experiments.

Research paper thumbnail of Appearance based deep domain adaptation for the classification of aerial images

ISPRS Journal of Photogrammetry and Remote Sensing

Research paper thumbnail of A New Approach for an Incremental Orientation of Micro-Uav Image Sequences

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Civil applications for small size unmanned aerial vehicles (UAV) have become quite important in r... more Civil applications for small size unmanned aerial vehicles (UAV) have become quite important in recent years and so have accurate orientation and navigation of these devices in unknown terrain. In this work we focus on on-line compatible positioning in facade observation based on monocular low resolution still images acquired by a camera mounted on a UAV. Also, a 3D point cloud of the facade is generated. This allows further processing steps, e. g. navigation assistance, collision avoidance or the evaluation of the point cloud density, verifying completeness of the data. To be able to deal with the increasing amount of observations and unknown parameters we implement an incremental bundle adjustment based on automatically determined tie points and sliding image triplets. The tripletwise orientation allows for an efficient double cross-check of the detected feature points and hence guarantees reliable initial values for the nonlinear bundle adjustment. The initial values are estimated within a convex formulation delivering a sound basis for the incremental adjustment. Our algorithm is evaluated by means of imagery we took of the facade of the Welfenschloss in Hannover, captured from a manually flown Microdrones md4-200 micro-UAV. We compare the orientation results of our approach with an approach in which initial values for the unknown object coordinates are computed algebraically. * Corresponding author we adaptively remove already oriented images from incremental adjustment based on the number of supporting object points measured in the related image to keep computation time constant.

Research paper thumbnail of Boosted Unsupervised Multi-Source Selection for Domain Adaptation

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences

Supervised machine learning needs high quality, densely sampled and labelled training data. Trans... more Supervised machine learning needs high quality, densely sampled and labelled training data. Transfer learning (TL) techniques have been devised to reduce this dependency by adapting classifiers trained on different, but related, (source) training data to new (target) data sets. A problem in TL is how to quantify the relatedness of a source quickly and robustly, because transferring knowledge from unrelated data can degrade the performance of a classifier. In this paper, we propose a method that can select a nearly optimal source from a large number of candidate sources. This operation depends only on the marginal probability distributions of the data, thus allowing the use of the often abundant unlabelled data. We extend this method to multi-source selection by optimizing a weighted combination of sources. The source weights are computed using a very fast boosting-like optimization scheme. The run-time complexity of our method scales linearly in regard to the number of candidate sou...

Research paper thumbnail of Conditional Random Fields for the Classification of Lidar Point Clouds

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

In this paper we propose a probabilistic supervised classification algorithm for LiDAR (Light Det... more In this paper we propose a probabilistic supervised classification algorithm for LiDAR (Light Detection And Ranging) point clouds. Several object classes (i.e. ground, building and vegetation) can be separated reliably by considering each point's neighbourhood. Based on Conditional Random Fields (CRF) this contextual information can be incorporated into classification process in order to improve results. Since we want to perform a point-wise classification, no primarily segmentation is needed. Therefore, each 3D point is regarded as a graph's node, whereas edges represent links to the nearest neighbours. Both nodes and edges are associated with features and have effect on the classification. We use some features available from full waveform technology such as amplitude, echo width and number of echoes as well as some extracted geometrical features. The aim of the paper is to describe the CRF model setup for irregular point clouds, present the features used for classification, and to discuss some results. The resulting overall accuracy is about 94 %.

Research paper thumbnail of Accurate Matching and Reconstruction of Line Features from Ultra High Resolution Stereo Aerial Images

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

In this study, a new reconstruction approach is proposed for the line segments that are nearly-al... more In this study, a new reconstruction approach is proposed for the line segments that are nearly-aligned (≤ 10º) with the epipolar line. The method manipulates the redundancy inherent in line pair-relations to generate artificial 3D point entities and utilize those entities during the estimation process to improve the height values of the reconstructed line segments. The best point entities for the reconstruction are selected based on a newly proposed weight function. To test the performance of the proposed approach, we selected three test patches over a built up area of the city of Vaihingen-Germany. Based on the results, the proposed approach produced highly promising reconstruction results for the line segments that are nearly-aligned with the epipolar line.

Research paper thumbnail of Rooftop Rainwater Harvesting for Mombasa: Scenario Development with Image Classification and Water Resources Simulation

Water

Mombasa faces severe water scarcity problems. The existing supply is unable to satisfy the demand... more Mombasa faces severe water scarcity problems. The existing supply is unable to satisfy the demand. This article demonstrates the combination of satellite image analysis and modelling as tools for the development of an urban rainwater harvesting policy. For developing a sustainable remedy policy, rooftop rainwater harvesting (RRWH) strategies were implemented into the water supply and demand model WEAP (Water Evaluation and Planning System). Roof areas were detected using supervised image classification. Future population growth, improved living standards, and climate change predictions until 2035 were combined with four management strategies. Image classification techniques were able to detect roof areas with acceptable accuracy. The simulated annual yield of RRWH ranged from 2.3 to 23 million cubic meters (MCM) depending on the extent of the roof area. Apart from potential RRWH, additional sources of water are required for full demand coverage.

Research paper thumbnail of Network Detection in Raster Data Using Marked Point Processes

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

We propose a new approach for the automatic detection of network structures in raster data. The m... more We propose a new approach for the automatic detection of network structures in raster data. The model for the network structure is represented by a graph whose nodes and edges correspond to junction-points and to connecting line segments, respectively; nodes and edges are further described by certain parameters. We embed this model in the probabilistic framework of marked point processes and determine the most probable configuration of objects by stochastic sampling. That is, different graph configurations are constructed randomly by modifying the graph entity parameters, by adding and removing nodes and edges to/ from the current graph configuration. Each configuration is then evaluated based on the probabilities of the changes and an energy function describing the conformity with a predefined model. By using the Reversible Jump Markov Chain Monte Carlo sampler, a global optimum of the energy function is determined. We apply our method to the detection of river and tidal channel ne...

Research paper thumbnail of Contextual Classification of Point Cloud Data by Exploiting Individual 3D Neigbourhoods

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote ... more The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on…

Research paper thumbnail of Report: Object Extraction for 3D City Models, Road Databases, and Traffic Monitoring - Concepts, Algorithms, and Evaluation

Research paper thumbnail of CMRT09 Object Extraction for 3D City Models, Road Databases, and Traffic Monitoring - Concepts, Algorithms, and Evaluation

and Remote Sensing. Reproduction of this volume or any parts thereof (excluding short quotations ... more and Remote Sensing. Reproduction of this volume or any parts thereof (excluding short quotations for the use in the preparation of reviews and technical and scientific papers) may be made only after obtaining the specific approval of the publisher. The papers appearing in this volume reflect the authors' opinions. Their inclusion in this publication does not necessarily constitute endorsement by the editors or by the publisher. Authors retain all rights to individual papers.

Research paper thumbnail of Probabilistic Multi-Person Tracking Using Dynamic Bayes Networks

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015

Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on... more Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicl...

Research paper thumbnail of Transfer Learning Based on Logistic Regression

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015

In this paper we address the problem of classification of remote sensing images in the framework ... more In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer learning in remote sensing on the basis of logistic regression. Logistic regression is a discriminative probabilistic classifier of low computational complexity, which can deal with multiclass problems. This research area deals with methods that solve problems in which labelled training data sets are assumed to be available only for a source domain, while classification is needed in the target domain with different, yet related characteristics. Classification takes place with a model of weight coefficients for hyperplanes which separate features in the transformed feature space. In term of logistic regression, our domain adaptation method adjusts the model parameters by iterative labelling of the target test data set. These labelled data features are iteratively added...

Research paper thumbnail of Semi-automatic verification of cropland and grassland using very high resolution mono-temporal satellite images

ISPRS Journal of Photogrammetry and Remote Sensing, 2014

ABSTRACT Many public and private decisions rely on geospatial information stored in a GIS databas... more ABSTRACT Many public and private decisions rely on geospatial information stored in a GIS database. For good decision making this information has to be complete, consistent, accurate and up-to-date. In this paper we introduce a new approach for the semi-automatic verification of a specific part of the, possibly outdated GIS database, namely cropland and grassland objects, using mono-temporal very high resolution (VHR) multispectral satellite images. The approach consists of two steps: first, a supervised pixel-based classification based on a Markov Random Field is employed to extract image regions which contain agricultural areas (without distinction between cropland and grassland), and these regions are intersected with boundaries of the agricultural objects from the GIS database. Subsequently, GIS objects labelled as cropland or grassland in the database and showing agricultural areas in the image are subdivided into different homogeneous regions by means of image segmentation, followed by a classification of these segments into either cropland or grassland using a Support Vector Machine. The classification result of all segments belonging to one GIS object are finally merged and compared with the GIS database label. The developed approach was tested on a number of images. The evaluation shows that errors in the GIS database can be significantly reduced while also speeding up the whole verification task when compared to a manual process.

Research paper thumbnail of Classification of urban LiDAR data using conditional random field and random forests

Joint Urban Remote Sensing Event 2013, 2013

ABSTRACT In this work we address the task of contextual classification of an airborne LiDAR point... more ABSTRACT In this work we address the task of contextual classification of an airborne LiDAR point cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random Field (CRF) framework. A CRF has been shown to deliver good results discerning multiple classes. It is a flexible approach for obtaining a reliable classification even in complex urban scenes. The incorporation of multi-scale features improves the results further. Based on the classification results, 2D building and tree objects are generated and evaluated by the benchmark of ISPRS WG III/4.

Research paper thumbnail of Advanced methods for automated object extraction from LiDAR in urban areas

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012

This paper gives an overview about advanced techniques for classification and object detection th... more This paper gives an overview about advanced techniques for classification and object detection that are being adopted for urban object detection from LiDAR data. The paper covers local supervised classifiers such as AdaBoost, SVM and Random Forests, statistical models of context such as Markov Random Fields and Conditional Random Fields, and sampling techniques. The relevance of features is also discussed. Applications include DTM generation and the extraction of buildings, trees, and low vegetation.

Research paper thumbnail of Classification of multitemporal remote sensing data using Conditional Random Fields

2010 IAPR Workshop on Pattern Recognition in Remote Sensing, 2010

Research paper thumbnail of Articles in Press–available online

Research paper thumbnail of A Dynamic Bayes Network for visual Pedestrian Tracking

Many tracking systems rely on independent single frame detections that are handled as observation... more Many tracking systems rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach suggests a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, prior scene information, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forests-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available dataset captured in a challenging outdoor scenario. Using the adaptive classifier, our system is able to keep track of pedestrians over long distances while at the same time supporting the localisation of the people. The results show that the derived trajectories achieve a geometric accuracy superior to the one achieved by modelling the image positions as observations.

Research paper thumbnail of Extraction of fluvial networks in lidar data using marked point processes

We propose a method for the automatic extraction of fluvial networks in lidar data with the aim t... more We propose a method for the automatic extraction of fluvial networks in lidar data with the aim to obtain a connected network represented by the fluvial channels' skeleton. For that purpose we develop a two-step approach. First, we fit rectangles to the data using a stochastic optimization based on a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler and simulated annealing. High gradients on the rectangles' border and non-overlapping areas of the objects are introduced as model in the optimization process. In a second step, we determine the principal axes of the rectangles and their intersection points. Based on this a network graph is constructed in which nodes represent junction points or end points, respectively, and edges in-between straight line segments. We evaluate our method on lidar data with a tidal channel network and show some preliminary results.

Research paper thumbnail of Experiences with the Digital Photogrammetric Program Package Orpheus based on CIPA's „Zurich City Hall&unknown; Dataset for Architectural Photogrammetry

Experiences with the digital photogrammetric program package ORPHEUS based on CIPA's « Zurich cit... more Experiences with the digital photogrammetric program package ORPHEUS based on CIPA's « Zurich city hall » dataset for architectural photogrammetry.