Foreword to the special issue on pattern recognition in remote sensing

Cataloged from PDF version of article.The nine papers in this special issue focus on covering different aspects of remote sensing image analysis. © 2012 IEE

Foreword to the special issue on pattern recognition in remote sensing

[No abstract available

TSML: A XML-based Format for Exchange of Training Samples for Pattern Recognition in Remote Sensing Images

The availability of large and complex data sets has shifted the focus of pattern recognition towards developing techniques that can efficiently handle these types of data sets. For example, Multiple Classifier Systems claim their ability in reducing the error and complexity of classification by partitioning the data space and combining classifiers predictions. However, it is not an easy task to generate several partitions and moreover to use them in an efficient manner. Another difficult aspect is related to the exchange of training data in different formats among systems to combine classifiers of different and heterogeneous systems. This paper presents a model and structure of training samples based on XML (eXtensible Markup Language) to facilitate the partitioning and exchange among different image classification system. The main contribution is to apply the flexibility of XML that addresses interoperability and communication among heterogeneous systems in partitioning data sets as well as to facilitate interchange of such sets among image processing and pattern recognition systems

Guest editorial: Foreword to the special issue on pattern recognition in remote sensing

[No abstract available

An introduction to quantitative remote sensing

The quantitative approach to remote sensing is discussed along with the analysis of remote sensing data. Emphasis is placed on the application of pattern recognition in numerically oriented remote sensing systems. A common background and orientation for users of the LARS computer software system is provided

Performance evaluation of building detection and digital surface model extraction algorithms: Outcomes of the PRRS 2008 algorithm performance contest

This paper presents the initial results of the Algorithm Performance Contest that was organized as part of the 5th IAPRWorkshop on Pattern Recognition in Remote Sensing (PRRS 2008). The focus of the 2008 contest was automatic building detection and digital surface model (DSM) extraction. A QuickBird data set with manual ground truth was used for building detection evaluation, and a stereo Ikonos data set with a highly accurate reference DSM was used for DSM extraction evaluation. Nine submissions were received for the building detection task, and three submissions were received for the DSM extraction task. We provide an overview of the data sets, the summaries of the methods used for the submissions, the details of the evaluation criteria, and the results of the initial evaluation. © 2008 IEEE

Automatic and semi-automatic extraction of curvilinear features from SAR images

Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images