Earth observation satellites (EOS) such as Landsat provide image datasets that can be immensely useful in numerous application domains. One way of analyzing satellite images for land use and land cover change (LULCC) is time series analysis (TSA). Several algorithms for time series analysis have been proposed by various groups in remote sensing; more algorithms (that can be adapted) are available in the general time series literature. However, in spite of an abundance of algorithms, the choice of algorithm to be used for analyzing an image stack is presently an open question. A concurrent issue is the prohibitive size of Landsat datasets, currently of the order of petabytes and growing. This makes them computationally unwieldy --- both in storage and processing. An EOS image stack typically consists of multiple images of a fixed area on the Earth's surface (same latitudes and longitudes) taken at different time points. Experiments on multicore servers indicate that carrying out meaningful time series analysis on one such interannual, multitemporal stack with existing state of the art codes can take several days.
This work proposes using multiple algorithms to analyze a given image stack in a polyalgorithmic framework. A polyalgorithm combines several basic algorithms, each meant to solve the same problem, producing a strategy that unites the strengths and circumvents the weaknesses of constituent algorithms. The foundation of the proposed TSA based polyalgorithm is laid using three algorithms (LandTrendR, EWMACD, and BFAST). These algorithms are precisely described mathematically, and chosen to be fundamentally distinct from each other in design and in the phenomena they capture. Analysis of results representing success, failure, and parameter sensitivity for each algorithm is presented. Scalability issues, important for real simulations, are also discussed, along with scalable implementations, and speedup results. For a given pixel, Hausdorff distance is used to compare the distance between the change times (breakpoints) obtained from two different algorithms. Timesync validation data, a dataset that is based on human interpretation of Landsat time series in concert with historical aerial photography, is used for validation. The polyalgorithm yields more accurate results than EWMACD and LandTrendR alone, but counterintuitively not better than BFAST alone. This nascent work will be directly useful in land use and land cover change studies, of interest to terrestrial science research, especially regarding anthropogenic impacts on the environment, and in much broader applications such as health monitoring and urban transportation. / M. S. / Numerous manmade satellites circling around the Earth regularly take pictures (images) of the Earth’s surface from up above. These images naturally provide information regarding the land cover of any given piece of land at the moment of capture (for e.g., whether the land area in the picture is covered with forests or with agriculture or housing). Therefore, for a fixed land area, if a person looks at a chronologically arranged series of images, any significant changes in land use can be identified. Identifying such changes is of critical importance, especially in this era where deforestation, urbanization, and global warming are major concerns.
The goal of this thesis is to investigate the design of methodologies (algorithms) that can efficiently and accurately use satellite images for answering questions regarding land cover trend and change. Experience shows that the state-of-the-art methodologies produce great results for the region they were originally designed on but their performance on other regions is unpredictable. In this work, therefore, a ‘polyalgorithm’ is proposed. A ‘polyalgorithm’ utilizes multiple simple methodologies and strategically combines them so that the outcome is better than the individual components. In this introductory work, three component methodologies are utilized; each component methodology is capable of capturing phenomenon different from the other two. Mathematical formulation of each component methodology is presented. Initial strategy for combining the three component algorithms is proposed. The outcomes of each component methodology as well the polyalgorithm are tested on human interpreted data. The strengths and limitations of each methodology are also discussed. Efficiency of the codes used for implementing the polyalgorithm is also discussed; this is important because the satellite data that needs to be processed is known to be huge (petabytes sized already and growing). This nascent work will be directly useful especially in understanding the impact of human activities on the environment. It will also be useful in other applications such as health monitoring and urban transportation.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/93177 |
| Date | 23 February 2018 |
| Creators | Saxena, Rishu |
| Contributors | Computer Science, Watson, Layne T., Wynne, Randolph H., Raghvendra, Sharath |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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