Supervised classification and improved filtering method for shoreline detection

Shoreline monitoring is important to overcome the problems in the measurement of the shoreline. Recently, many researchers have directed attention to methods of predicting shoreline changes by the use of multispectral images. However, the images being captured tend to have several problems due to the weather. Therefore, identification of multi class features which includes vegetation and shoreline using multispectral satellite image is one of the challenges encountered in the detection of shoreline. An efficient framework using the near infrared–histogram equalisation and improved filtering method is proposed to enhance the detection of the shoreline in Tanjung Piai, Malaysia, by using SPOT-5 images. Sub-pixel edge detection and the Wallis filter are used to compute the edge location with the subpixel accuracy and reduce the noise. Then, the image undergoes image classification process by using Support Vector Machine. The proposed method performed more effectively and reliable in preserving the missing line of the shoreline edge in the SPOT-5 images

Visualization and Analysis of Flow Fields based on Clifford Convolution

Vector fields from flow visualization often containmillions of data values. It is obvious that a direct inspection of the data by the user is tedious. Therefore, an automated approach for the preselection of features is essential for a complete analysis of nontrivial flow fields. This thesis deals with automated detection, analysis, and visualization of flow features in vector fields based on techniques transfered from image processing. This work is build on rotation invariant template matching with Clifford convolution as developed in the diploma thesis of the author. A detailed analysis of the possibilities of this approach is done, and further techniques and algorithms up to a complete segmentation of vector fields are developed in the process. One of the major contributions thereby is the definition of a Clifford Fourier transform in 2D and 3D, and the proof of a corresponding convolution theorem for the Clifford convolution as well as other major theorems. This Clifford Fourier transform allows a frequency analysis of vector fields and the behavior of vectorvalued filters, as well as an acceleration of the convolution computation as a fast transform exists. The depth and precision of flow field analysis based on template matching and Clifford convolution is studied in detail for a specific application, which are flow fields measured in the wake of a helicopter rotor. Determining the features and their parameters in this data is an important step for a better understanding of the observed flow. Specific techniques dealing with subpixel accuracy and the parameters to be determined are developed on the way. To regard the flow as a superposition of simpler features is a necessity for this application as close vortices influence each other. Convolution is a linear system, so it is suited for this kind of analysis. The suitability of other flow analysis and visualization methods for this task is studied here as well. The knowledge and techniques developed for this work are brought together in the end to compute and visualize feature based segmentations of flow fields. The resulting visualizations display important structures of the flow and highlight the interesting features. Thus, a major step towards robust and automatic detection, analysis and visualization of flow fields is taken

A review on shoreline detection framework using remote sensing satellite image

Shoreline is usually defined as the intersection of the land with the water surface of the mean high water line with the beach profile. In relation, most research in recent years has set the focus on remote sensing which makes it possible to collect data on this shoreline areas. Furthermore, shoreline detection is the ability to recognise and evaluate shoreline detection, so that facilitates decision makers to adapt, mitigate and manage the shoreline risks. Thus, this paper aims to investigate current works on shoreline detection framework using remote sensing satellite images. This investigation includes current research trends on the computational method in shoreline detection, image segmentation, and image filtering method

A review on shoreline detection framework using remote sensing satellite image

Shoreline is usually defined as the intersection of the land with the water surface of the mean high water line with the beach profile. In relation, most research in recent years has set the focus on remote sensing which makes it possible to collect data on this shoreline areas. Furthermore, shoreline detection is the ability to recognise and evaluate shoreline detection, so that facilitates decision makers to adapt, mitigate and manage the shoreline risks. Thus, this paper aims to investigate current works on shoreline detection framework using remote sensing satellite images. This investigation includes current research trends on the computational method in shoreline detection, image segmentation, and image filtering method

Algorithms for Building High-Accurate Optical Tracking Systems

Die vorliegende Arbeit präsentiert eine Untersuchung von Einflussfaktoren auf die Genauigkeit eines optischen Trackingsystems zur hoch präzisen Koordinatenmessung, wie sie beispielsweise im Bereich der Computer-unterstützten Chirurgie benötigt wird. Zu den Haupteinflussfaktoren gehören die Modellierung der Aufnahmegeometrie, die verwendeten Bildverarbeitungsalgorithmen zur Markensegmentierung, welche sowohl während der Systemkalibrierung als auch während des eigentlichen Messvorgangs verwendet werden, und nicht zuletzt thermische Einflüsse.Wahrend die Modellierung der Kamerageometrie ein gut erforschter Gegenstand sowohl im Bereich der Photogrammetrie als auch des Maschinellen Sehens darstellt, existieren fur den Vergleich von verschiedenen Markentypen und deren Segmentierungsalgorithmen in bezug auf die Messgenauigkeit noch keine umfassenden Ergebnisse. Einen weiteren Bereich, der nahezu nicht untersucht ist, bilden thermische Einflüsse auf die zugrundeliegende Aufnahmegeometrie. Die vorliegende Arbeit legt ihren Schwerpunkt auf diese zwei Bereiche. Zum einen werden verschiedene Algorithmen zur Segmentierung von Messmarken vorgestellt und miteinander verglichen. Den zweiten großen Schwerpunkt bildet eine Analyse von thermischen Einflussen auf Kameras. Es wird ein Verfahren entwickelt, welches den Einfluss von Temperaturänderungen modelliert und so Messfehler kompensieren kann. Die Ergebnisse dieser Arbeit finden Anwendung in der Entwicklung eines optischen Trackingsystems fur den Einsatz in der orthopädischen Chirurgie

Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

Carried baggage detection and recognition in video surveillance with foreground segmentation

Security cameras installed in public spaces or in private organizations continuously record video data with the aim of detecting and preventing crime. For that reason, video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis, have gained high interest in recent years. In this thesis, the primary focus is on two key aspects of video analysis, reliable moving object segmentation and carried object detection & identification. A novel moving object segmentation scheme by background subtraction is presented in this thesis. The scheme relies on background modelling which is based on multi-directional gradient and phase congruency. As a post processing step, the detected foreground contours are refined by classifying the edge segments as either belonging to the foreground or background. Further contour completion technique by anisotropic diffusion is first introduced in this area. The proposed method targets cast shadow removal, gradual illumination change invariance, and closed contour extraction. A state of the art carried object detection method is employed as a benchmark algorithm. This method includes silhouette analysis by comparing human temporal templates with unencumbered human models. The implementation aspects of the algorithm are improved by automatically estimating the viewing direction of the pedestrian and are extended by a carried luggage identification module. As the temporal template is a frequency template and the information that it provides is not sufficient, a colour temporal template is introduced. The standard steps followed by the state of the art algorithm are approached from a different extended (by colour information) perspective, resulting in more accurate carried object segmentation. The experiments conducted in this research show that the proposed closed foreground segmentation technique attains all the aforementioned goals. The incremental improvements applied to the state of the art carried object detection algorithm revealed the full potential of the scheme. The experiments demonstrate the ability of the proposed carried object detection algorithm to supersede the state of the art method

SAR Image Edge Detection: Review and Benchmark Experiments

Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art