Recent Publications

in press

Jan Stefanski and Benjamin Mack and Björn Waske, "Optimization of object-based image analysis with Random Forests for land cover mapping", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. in press.

A prerequisite for object-based image analysis is the generation of adequate segments. However, the parameters for the image segmentation algorithms are often manually defined. Therefore, the generation of an ideal segmentation level is usually costly and user-depended. In this paper a strategy for a semi-automatic optimization of object-based classification of multitemporal data is introduced by using Random Forests (RF) and a novel segmentation algorithm. The Superpixel Contour (SPc) algorithm is used to generate a set of different levels of segmentation, using various combinations of parameters in a user-defined range. Finally, the best parameter combination is selected based on the cross-validation-like out-of-bag (OOB) error that is provided by RF. Therefore, the quality of the parameters and the corresponding segmentation level can be assessed in terms of the classification accuracy, without providing additional independent test data. To evaluate the potential of the proposed concept, we focus on land cover classification of two study areas, using multitemporal RapidEye and SPOT 5 images. A classification that is based on eCognition's widely used Multiresolution Segmentation algorithm (MRS) is used for comparison. Experimental results underline that the two segmentation algorithms SPc and MRS perform similar in terms of accuracy and visual interpretation. The proposed strategy that uses the OOB error for the selection of the ideal segmentation level provides similar classification accuracies, when compared to the results achieved by manual-based image segmentation. Overall, the proposed strategy is operational and easy to handle and thus economizes the findings of optimal segmentation parameters for the Superpixel Contour algorithm. 

@article{Stefanski2013Optimization,
  author = {Stefanski, Jan and Mack, Benjamin and Waske, Bj\"orn},
  title = {Optimization of object-based image analysis with Random Forests for land cover mapping},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {in press},
  doi = {10.1109/JSTARS.2013.2253089}
}

2012

W. Förstner, "Minimal Representations for Testing and Estimation in Projective Spaces", Z. f. Photogrammetrie, Fernerkundung und Geoinformation. Vol. 3, pp. 209-220. 2012.

Testing and estimation using homogeneous coordinates and matrices has to cope with obstacles such as singularities of covariance matrices and redundant parametrizations. The paper proposes a representation of the uncertainty of all types of geometric entities which (1) only requires the minimum number of parameters, (2) is free of singularities, (3) enables to exploit the simplicity of homogeneous coordinates to represent geometric constraints and (4) allows to handle geometric entities which are at infinity or at least very far away. We develop the concept, discuss its usefulness for bundle adjustment and demonstrate its applicability for determining 3D lines from observed image line segments in a multi view setup. 

@article{Forstner2012Minimal,
  author = {W. F\"orstner},
  title = {Minimal Representations for Testing and Estimation in Projective Spaces},
  journal = {Z. f. Photogrammetrie, Fernerkundung und Geoinformation},
  year = {2012},
  volume = {3},
  pages = {209--220},
  doi = {10.1127/1432-8364/2012/0112}
}

Sascha Klemenjak and Björn Waske and Sivia Valero and Jocelyn Chanussot, "Automatic Detection of Rivers in High-Resolution SAR Data", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing., October, 2012. Vol. 5(5), pp. 1364-1372. 2012.

Remote sensing plays a major role in supporting decision-making and surveying compliance of several multilateral environmental treaties. In this paper, we present an approach for supporting monitoring compliance of river networks in context of the European Water Framework Directive. Only a few approaches have been developed for extracting river networks from satellite data and usually they require manual input, which seems not feasible for automatic and operational application. We propose a method for the automatic extraction of river structures in TerraSAR-X data. The method is based on mathematical morphology and supervised image classification, using automatically selected training samples. The method is applied on TerraSAR-X images from two different study sites. In addition, the results are compared to an alternative method, which requires manual user interaction. The detailed accuracy assessment shows that the proposed method achieves accurate results (Kappa $ sim$ 0.7) and performs almost similar in terms of accuracy, when compared to the alternative approach. Moreover, the proposed method can be applied on various datasets (e.g., multitemporal, multisensoral and multipolarized) and does not require any additional user input. Thus, the highly flexible approach is interesting in terms of operational monitoring systems and large scale applications. 

@article{Klemenjak2012Automatic,
  author = {Klemenjak, Sascha and Waske, Bj\"orn and Valero, Sivia and Chanussot, Jocelyn},
  title = {Automatic Detection of Rivers in High-Resolution SAR Data},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2012},
  volume = {5},
  number = {5},
  pages = {1364--1372},
  doi = {10.1109/JSTARS.2012.2189099}
}

Ribana Roscher and Wolfgang Förstner and Björn Waske, "I2VM: Incremental import vector machines", Image and Vision Computing., May, 2012. Vol. 30(4-5), pp. 263-278. 2012.

We introduce an innovative incremental learner called incremental import vector machines ((IVM)-V-2). The kernel-based discriminative approach is able to deal with complex data distributions. Additionally, the learner is sparse for an efficient training and testing and has a probabilistic output. We particularly investigate the reconstructive component of import vector machines, in order to use it for robust incremental teaming. By performing incremental update steps, we are able to add and remove data samples, as well as update the current set of model parameters for incremental learning. By using various standard benchmarks, we demonstrate how (IVM)-V-2 is competitive or superior to other incremental methods. It is also shown that our approach is capable of managing concept-drifts in the data distributions. (C) 2012 Elsevier B.V. All rights reserved. 

@article{Roscher2012I2VM,
  author = {Roscher, Ribana and F\"orstner, Wolfgang and Waske, Bj\"orn},
  title = {I2VM: Incremental import vector machines},
  journal = {Image and Vision Computing},
  year = {2012},
  volume = {30},
  number = {4-5},
  pages = {263--278},
  doi = {10.1016/j.imavis.2012.04.004}
}

Ribana Roscher and Björn Waske and Wolfgang Förstner, "Incremental Import Vector Machines for Classifying Hyperspectral Data", IEEE Transactions on Geoscience and Remote Sensing., September, 2012. Vol. 50(9), pp. 3463-3473. 2012.

In this paper, we propose an incremental learning strategy for import vector machines (IVM), which is a sparse kernel logistic regression approach. We use the procedure for the concept of self-training for sequential classification of hyperspectral data. The strategy comprises the inclusion of new training samples to increase the classification accuracy and the deletion of noninformative samples to be memory and runtime efficient. Moreover, we update the parameters in the incremental IVM model without retraining from scratch. Therefore, the incremental classifier is able to deal with large data sets. The performance of the IVM in comparison to support vector machines (SVM) is evaluated in terms of accuracy, and experiments are conducted to assess the potential of the probabilistic outputs of the IVM. Experimental results demonstrate that the IVM and SVM perform similar in terms of classification accuracy. However, the number of import vectors is significantly lower when compared to the number of support vectors, and thus, the computation time during classification can be decreased. Moreover, the probabilities provided by IVM are more reliable, when compared to the probabilistic information, derived from an SVM's output. In addition, the proposed self-training strategy can increase the classification accuracy. Overall, the IVM and its incremental version is worthwhile for the classification of hyperspectral data. 

@article{Roscher2012Incremental,
  author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
  title = {Incremental Import Vector Machines for Classifying Hyperspectral Data},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2012},
  volume = {50},
  number = {9},
  pages = {3463--3473},
  doi = {10.1109/TGRS.2012.2184292}
}

Björn Waske and Sebastian van der Linden and Carsten Oldenburg and Benjamin Jakimow and Andreas Rabe and Patrick Hostert, "imageRF -- A user-oriented implementation for remote sensing image analysis with Random Forests", Environmental Modelling & Software., July, 2012. Vol. 35, pp. 192-193. 2012.

An IDL implementation for the classification and regression analysis of remote sensing images with Random Forests is introduced. The tool, called imageRF, is platform and license independent and uses generic image file formats. It works well with default parameterization, yet all relevant parameters can be defined in intuitive GUIs. This makes it a user-friendly image processing tool, which is implemented as an add-on in the free EnMAP-Box and may be used in the commercial IDL/ENVI software. (C) 2012 Elsevier Ltd. All rights reserved. 

@article{Waske2012imageRF,
  author = {Waske, Bj\"orn and van der Linden, Sebastian and Oldenburg, Carsten and Jakimow, Benjamin and Rabe, Andreas and Hostert, Patrick},
  title = {imageRF -- A user-oriented implementation for remote sensing image analysis with Random Forests},
  journal = {Environmental Modelling \& Software},
  year = {2012},
  volume = {35},
  pages = {192--193},
  doi = {10.1016/j.envsoft.2012.01.014}
}

Björn Waske and Jon Benediktsson and Johannes Sveinsson, "Signal and Image Processing for Remote Sensing", In Signal and Image Processing for Remote Sensing, Second Edition. Chen, Chi Hau (Eds.), February, 2012., pp. 365-374. CRC Press. 2012.

Land cover classifications are perhaps the widest used application in context of remote sensing. The recent development of remote sensing systems, including numerous bands, high spatial resolution and increased repetition rates as well as the availability of more diverse remote sensing imagery increase the potential of remote sensing based land cover classifications. Nevertheless, recent data sets demand more sophisticated classifiers and the development of adequate methods in an ongoing research topic in the field of remote sensing.
In this context the potential of the ensemble technique Random Forest (RF) for classifying hyperspectral and multisensor remote sensing data is demonstrated. The classification is done on two different data sets, comprising of (i) multispectral and SAR data and (ii) hyperspectral imagery. The results are compared to well known algorithms (e.g. Maximum Likelihood Classifier, Spectral Angle Mapper) as well as recent developments such as Support Vector Machines (SVM). Overall the results demonstrate that RF can be considered desirable for classification of hyperspectral as well as multisensor data sets. RF, significantly outperforms common methods in terms of accuracy and is comparable to SVM. RF achieve high accuracies, even with small training sample, and is simple to handle, because it mainly depends on two user-defined values. 

@inbook{Waske2012Signal,
  author = {Waske, Bj\"orn and Benediktsson, Jon and Sveinsson, Johannes},
  editor = {Chen, Chi Hau},
  title = {Signal and Image Processing for Remote Sensing},
  booktitle = {Signal and Image Processing for Remote Sensing, Second Edition},
  publisher = {CRC Press},
  year = {2012},
  pages = {365--374},
  edition = {2nd},
  doi = {10.1201/b11656-21}
}

Stefan Gehrig and Alexander Barth and Nicolai Schneider and Jan Siegemund, "A Multi-Cue Approach for Stereo-Based Object Confidence Estimation", In Intelligent Robots and Systems (IROS). Vilamoura, Portugal, pp. 3055 - 3060. 2012.

In this contribution we present an approach to compute object confidences for stereo-vision-based object tracking schemes. Meaningful object confidences help to reduce false alarm rates of safety systems and improve the downstream system performance for modules such as sensor fusion and situation analysis. Several cues from stereo vision and from the tracking process are fused in a Bayesian manner. An evaluation on a 38,000 frames urban drive shows the effectiveness of the approach compared to the same object tracking scheme with simple heuristics for the object confidence. Within the evaluation, also the relevance of occurring phantoms is considered by computing the collision risk. The proposed confidence measures reduce the number of predicted imminent collisions from 86 to 0 maintaining almost the same system availability. 

@inproceedings{Gehrig2012Multi,
  author = {Gehrig, Stefan and Barth, Alexander and Schneider, Nicolai and Siegemund, Jan},
  title = {A Multi-Cue Approach for Stereo-Based Object Confidence Estimation},
  booktitle = {Intelligent Robots and Systems (IROS)},
  year = {2012},
  pages = {3055 -- 3060},
  doi = {10.1109/IROS.2012.6385455}
}

Johannes Schneider and Falko Schindler and Thomas Läbe and Wolfgang Förstner, "Bundle Adjustment for Multi-camera Systems with Points at Infinity", In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. I-3, pp. 75-80. 2012.

We present a novel approach for a rigorous bundle adjustment for omnidirectional and multi-view cameras, which enables an efficient maximum-likelihood estimation with image and scene points at infinity. Multi-camera systems are used to increase the resolution, to combine cameras with different spectral sensitivities (Z/I DMC, Vexcel Ultracam) or - like omnidirectional cameras - to augment the effective aperture angle (Blom Pictometry, Rollei Panoscan Mark III). Additionally multi-camera systems gain in importance for the acquisition of complex 3D structures. For stabilizing camera orientations - especially rotations - one should generally use points at the horizon over long periods of time within the bundle adjustment that classical bundle adjustment programs are not capable of. We use a minimal representation of homogeneous coordinates for image and scene points. Instead of eliminating the scale factor of the homogeneous vectors by Euclidean normalization, we normalize the homogeneous coordinates spherically. This way we can use images of omnidirectional cameras with single-view point like fisheye cameras and scene points, which are far away or at infinity. We demonstrate the feasibility and the potential of our approach on real data taken with a single camera, the stereo camera FinePix Real 3D W3 from Fujifilm and the multi-camera system Ladybug3 from Point Grey. 

@inproceedings{Schneider2012Bundle,
  author = {Schneider, Johannes and Schindler, Falko and L\"abe, Thomas and F\"orstner, Wolfgang},
  title = {Bundle Adjustment for Multi-camera Systems with Points at Infinity},
  booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
  year = {2012},
  volume = {I-3},
  pages = {75--80},
  doi = {10.5194/isprsannals-I-3-75-2012}
}

Susanne Wenzel and Wolfgang Förstner, "Learning a compositional representation for facade object categorization", In ISPRS Annals of Photogrammetry, Remote Sensing and the Spatial Information Sciences; Proc. of 22nd Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS). Vol. I-3( 2012), pp. 197-202. 2012.

Our objective is the categorization of the most dominant objects in facade images, like windows, entrances and balconies. In order to execute an image interpretation of complex scenes we need an interaction between low level bottom-up feature detection and highlevel inference from top-down. A top-down approach would use results of a bottom-up detection step as evidence for some high-level inference of scene interpretation. We present a statistically founded object categorization procedure that is suited for bottom-up object detection. Instead of choosing a bag of features in advance and learning models based on these features, it is more natural to learn which features best describe the target object classes. Therefore we learn increasingly complex aggregates of line junctions in image sections from man-made scenes. We present a method for the classification of image sections by using the histogram of diverse types of line aggregates. 

@inproceedings{Wenzel2012Learning,
  author = {Wenzel, Susanne and F\"orstner, Wolfgang},
  title = {Learning a compositional representation for facade object categorization},
  booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and the Spatial Information Sciences; Proc. of 22nd Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS)},
  year = {2012},
  volume = {I-3},
  number = { 2012},
  pages = {197--202},
  doi = {10.5194/isprsannals-I-3-197-2012}
}
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