A fuzzy decision tree for processing satellite images and landsat data

  1. Get@NRC: A fuzzy decision tree for processing satellite images and landsat data (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1016/j.procs.2015.05.157
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Proceedings titleProcedia Computer Science
ConferenceThe International Conference on Ambient Systems, Networks and Technologies, ANT-2015, the International Conference on Sustainable Energy Information Technology, SEIT-2015, 2 June 2015 through 5 June 2015
Pages11921197; # of pages: 6
SubjectAlgorithms; Climate change; Complex networks; Data handling; Data mining; Decision trees; Geographic information systems; Image classification; Image processing; Pixels; Remote sensing; Satellites; Trees (mathematics); Anthropogenic activity; Data processing algorithms; Decision-tree algorithm; Fuzzy classification; LANDSAT; Multi-criteria classification; PROAFTN; Satellite images; Classification (of information)
AbstractSatellite and airborne images, including Landsat, ASTER, and Hyperspectral data, are widely used in remote sensing and Geographic Information Systems (GIS) to understand natural earth related processes, climate change, and anthropogenic activity. The nature of this type of data is usually multi or hyperspectral with individual spectral bands stored in raster file structures of large size and global coverage. The elevated number of bands (on the order of 200 to 250 bands) requires data processing algorithms capable of extracting information content, removing redundancy. Conventional statistical methods have been devised to reduce dimensionality however they lack specific processing to handle data diversity. Hence, in this paper we propose a new data analytic technique to classify these complex multidimensional data cubes. Here, we use a well-known database consisting of multi-spectral values of pixels from satellite images, where the classification is associated with the central pixel in each neighborhood. The goal of our proposed approach is to predict this classification based on the given multi-spectral values. To solve this classification problem, we propose an improved decision tree (DT) algorithm based on a fuzzy approach. More particularly, we introduce a new hybrid classification algorithm that utilizes the conventional decision tree algorithm enhanced with the fuzzy approach. We propose an improved data classification algorithm that utilizes the best of a decision tree and multi-criteria classification. To investigate and evaluate the performance of our proposed method against other DT classifiers, a comparative and analytical study is conducted on well-known Landsat data.
Publication date
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number21276964
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Record identifier214501a2-9843-4d3d-85a3-3f89766de669
Record created2015-11-10
Record modified2016-05-09
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