Segmentation

There is a need to automate terrain feature mapping so that to make the process more objective and less time consuming by using proper feature extraction techniques. The objective of this study was the use of object-oriented image analysis methods for the automatic extraction of alluvial fan terrain units. The study area was located in the Death Valley, Nevada, USA. The data used included an ASTER L1 satellite image and the 1 o Digital Elevation Model. The methodology developed for alluvial fan extraction included preprocessing of the digital data: filtering of the Digital Elevation Model (DEM) for noise removal, a Fourier Transform Wedge filter for the elimination of striping in the ASTER data and geometric co-registration of the satellite and DEM data. A multiresolution segmentation technique was then developed, delivering object primitives at four resolution levels. At the first and finest level, three physiographic feature types (basins, piedmonts and mountains) were extracted from the DEM to be used in the rule-based fuzzy classification of the following levels. Then, a knowledge base including definitions of Alluvial materials, Mountains, Basin floor salt deposits and Basin floor sediments was implemented. The second level was classified by the nearest neighbour classifier using spectral information for the first iteration of the classification procedure. For a second iteration, the knowledge base was further expanded primarily with heuristics concerning contextual information of the alluvial materials related to the geomorphological features extracted at the first level. Finally, in the last level, a projection was made, classifying the image into two classes: Alluvial Fans and Not Alluvial fans. The method gave good results in detecting alluvial fan units, working best for large shape alluvial fans. Some minor problems were encountered for the smaller alluvial fans, due to the difficulty of their boundar

Semantic Trajectory Data Mining: a User Driven Approach

Trajectories left behind cars, humans, birds or any other moving object are a new kind of data which can be very useful in decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user\u27s point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining