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  • Uhlmann, Stefan  (9)
Language
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  • 1
    Language: English
    In: ISPRS Journal of Photogrammetry and Remote Sensing, April 2014, Vol.90, pp.10-22
    Description: Fully and partially polarimetric SAR data in combination with textural features have been used extensively for terrain classification. However, there is another type of visual feature that has so far been neglected from polarimetric SAR classification: Color. It is a common practice to visualize polarimetric SAR data by color coding methods and thus it is possible to extract powerful color features from such pseudo color images so as to gather additional crucial information for an improved terrain classification. In this paper, we investigate the application of several individual visual features over different pseudo color generated images along with the traditional SAR and texture features for a novel supervised classification application of dual- and single-polarized SAR data. We then draw the focus on evaluating the effects of the applied pseudo coloring methods on the classification performance. An extensive set of experiments show that individual visual features or their combination with traditional SAR features introduce a new level of discrimination and provide noteworthy improvement of classification accuracies within the application of land use and land cover classification for dual- and single-pol image data.
    Keywords: Synthetic Aperture Radar ; Classification ; Image Analysis ; Visual Features ; Color ; Texture ; Engineering ; Geography
    ISSN: 0924-2716
    E-ISSN: 1872-8235
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  • 2
    Language: English
    In: IEEE Transactions on Geoscience and Remote Sensing, April 2014, Vol.52(4), pp.2197-2216
    Description: Polarimetric synthetic aperture radar (PolSAR) data are used extensively for terrain classification applying SAR features from various target decompositions and certain textural features. However, one source of information has so far been neglected from PolSAR classification: Color. It is a common practice to visualize PolSAR data by color coding methods and thus, it is possible to extract powerful color features from such pseudocolor images so as to provide additional data for a superior terrain classification. In this paper, we first review previous attempts for PolSAR classifications using various feature combinations and then we introduce and perform in-depth investigation of the application of color features over the Pauli color-coded images besides SAR and texture features. We run an extensive set of comparative evaluations using 24 different feature set combinations over three images of the Flevoland- and the San Francisco Bay region from the RADARSAT-2 and the AIRSAR systems operating in C- and L-bands, respectively. We then consider support vector machines and random forests classifier topologies to test and evaluate the role of color features over the classification performance. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared with the traditionally employed PolSAR and texture features) within the application of land use and land cover classification.
    Keywords: Classification ; Color Features ; Evaluation ; Feature Extraction ; Polarimetric Radar ; Synthetic Aperture Radar (SAR) ; Engineering ; Physics
    ISSN: 0196-2892
    E-ISSN: 1558-0644
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  • 3
    Language: English
    In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), August 2012, Vol.42(4), pp.1169-1186
    Description: Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a "Divide and Conquer" type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field.
    Keywords: Neurons ; Scattering ; Covariance Matrix ; Training ; Matrix Decomposition ; Indexes ; Measurement ; Evolutionary Classifiers ; Multidimensional Particle Swarm Optimization (MD-Pso) ; Polarimetric Synthetic Aperture Radar (SAR) ; Sciences (General) ; Engineering
    ISSN: 1083-4419
    E-ISSN: 1941-0492
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  • 4
    Language: English
    In: Remote Sensing, 01 May 2014, Vol.6(6), pp.4801-4830
    Description: In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current systems has become fairly large considering high- and very-high resolution data; yet on the other hand, the process of obtaining the ground truth data may be cumbersome for such large repositories. In this paper, we investigate the application of semi-supervised learning approaches and particularly focus on the small sample size problem. To that extend, we consider two basic unsupervised approaches by enlarging the initial labeled training set as well as an ensemble-based self-training method. We propose different strategies within self-training on how to select more reliable candidates from the pool of unlabeled samples to speed-up the learning process and to improve the classification performance of the underlying classifier ensemble. We evaluate the effectiveness of the proposed semi-supervised learning approach over polarimetric SAR data. Results show that the proposed self-training approach using an ensemble-based classifier that is initially trained over a small training set can achieve a similar performance level of a fully supervised learning approach where the training is performed over significantly larger labeled data. Considering the difficulties of the manual data labeling in such massive volumes of SAR repositories, this is indeed a promising accomplishment for semi-supervised SAR classification.
    Keywords: Semi-Supervised ; Machine Learning ; Ensemble ; SAR ; Superpixel ; Geography
    E-ISSN: 2072-4292
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  • 5
    Language: English
    In: EURASIP Journal on Advances in Signal Processing, 2010, Vol.2009(1), pp.1-13
    Description: Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD) PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF) technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.
    Keywords: Engineering;
    E-ISSN: 1687-6180
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  • 6
    Language: English
    In: 2011 19th European Signal Processing Conference, August 2011, pp.1160-1164
    Description: Polarimetric SAR image classification has been an active research field where several features and classifiers have been proposed in the past. Using numerous features can be a desirable option so as to achieve a better discrimination over certain classes, yet key questions such as how to avoid "Curse of Dimensionality" and how to combine them in the most effective way still remains unanswered. In this paper, we investigate SAR image classification using a large set of features, where the focus is particularly drawn on the extension of image processing features e.g. texture, edge and color. We propose a dedicated application of the Collective Network of (evolutionary) Binary Classifiers (CNBC) framework to address these problems with the aim of achieving high feature scalability. We furthermore tested several SAR and image processing feature constellations over three well-known SAR image classifiers and make comparative evaluations with CNBC. Experimental results over the full polarimetric AIRSAR San Francisco Bay and Flevoland images show that additional image processing features are able to improve SAR image classification accuracy and moreover, the CNBC proves useful and can scale well especially whenever high number of features and classes are encountered.
    Keywords: Synthetic Aperture Radar ; Image Color Analysis ; Feature Extraction ; Support Vector Machines ; Scattering ; Matrix Decomposition
    ISSN: 2076-1465
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
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  • 7
    Language: English
    In: 2007 15th European Signal Processing Conference, September 2007, pp.1804-1808
    Description: Utilizing regionalized features in Content-based Image Retrieval (CBIR) has been a dynamic research area over the past years. Several systems have been developed using their specific segmentation and feature extraction methods. In this paper, a strategy to model a regionalized CBIR framework is presented. Here, segmentation and local feature extraction are not specified and considered as "blackboxes", which allows application of any segmentation method and visual descriptors. The proposed framework further adopts a grouping approach in order to "correct" possible over segmentation faults and a spatial feature called region proximity to describe regions topology in a visual scenery by a block-based approach. Using the MUVIS framework the proposed approach is developed and tested as feature extraction module, and its retrieval performance is compared against two frame-based color-texture descriptors. Experiments are carried out on synthetic and natural image databases and results indicate that a promising retrieval performance can be obtained if the segmentation quality is reasonable; however texture descriptors in general are degraded whenever applied on arbitrary-shape regions.
    Keywords: Image Segmentation ; Image Color Analysis ; Feature Extraction ; Databases ; Shape ; Signal Processing ; Visualization ; Engineering
    ISBN: 9788392134046
    ISBN: 8392134044
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
    Source: IEEE eBooks
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  • 8
    Description: Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011...
    Source: DataCite
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  • 9
    Description: Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007...
    Source: DataCite
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