A vast amount of digital satellite and aerial images are collected over time, which calls for techniques to extract useful high-level information, such as recognizable events. One part of this thesis proposes a framework for streaming analysis of the time series, which can recognize events without supervision and memorize them by building the temporal contexts. The memorized historical data is then used to predict the future and detect anomalies. A new incremental clustering method is proposed to recognize the event without training. A memorization method of double localization, including relative and absolute localization, is proposed to model the temporal context. Finally, the predictive model is built based on the method of memorization. The "Edinburgh Pedestrian Dataset", which offers about 1000 observed trajectories of pedestrians detected in camera images each working day for several months, is used as an example to illustrate the framework.
Although there is a large amount of image data captured, most of them are not available to the public. The other part of this thesis developed a method of generating spatial-spectral-temporal synthetic images by enhancing the capacity of a current tool called DIRISG (Digital Imaging and Remote Sensing Image Generation). Currently, DIRSIG can only model limited temporal signatures. In order to observe general temporal changes in a process within the scene, a process model, which links the observable signatures of interest temporally, should be developed and incorporated into DIRSIG. The sub process models could be categorized into two types. One is that the process model drives the property of each facet of the object changing over time, and the other one is to drive the geometry location of the object in the scene changing as a function of time. Two example process models are used to show how process models can be incorporated into DIRSIG.
Library of Congress Subject Headings
Remote sensing--Data processing; Artificial satellites in remote sensing; Image analysis; Image processing--Digital techniques
Imaging Science (Ph.D.)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Sun, Jiangqin, "Temporal Signature Modeling and Analysis" (2014). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus