Authors / CoAuthors
Tan, P. | Sagar, S. | Mueller, N. | Lymburner, L. | Thankappan, M. | Lewis, A.
Abstract
We propose a surface cover change detection system based on the Australian Geoscience Data Cube (AGDC). The AGDC is a common analytical framework for large volumes of regularly gridded geoscientific data initially developed by Geoscience Australia (GA). AGDC effectively links geoscience data sets from various sources by spatial and temporal stamps associated with the data. Therefore, AGDC enables analysis of generations of consistent remote sensing time series data across Australia. The Australian Reflectance Grid 25m is one of the remote sensing data sets in the AGDC. The data is currently hosted at the high performance computational cloud at the National Computational Infrastructure. The proposed change detection system takes advantage of temporally rich data in the AGDC, applying time series analysis to identify changes in surface cover. The proposed system consists of various modules, which are independent of each other. The modules include: - a pixel quality mask and time series noise detection mask, which detects and filters out noise in data; - spectral classification modules based on random forests algorithm, which classifies pixels into specific objects using spectral information; - training modules which create classification modules using known surface cover data; - time series analysis modules, which models and transforms time series data into coefficients relevant to change detection targets; - temporal and spatial classification modules, which classify pixels into predefined land cover classes. A typical work flow for a change detection system includes sequential integration of the above mentioned modules. The system has been tested for applications in shallow water coastal zones and reforestation / deforestation detection, and displays a good potential for further development. This paper summarises development of the work flow and the initial results from example applications, such as reforestation / deforestation detection.
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nonGeographicDataset
eCat Id
83193
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- External PublicationConference Paper
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Earth Sciences
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- Published_Internal
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2015-01-01T00:00:00
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