Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

Sarker, Chandrama Dey, Jia, X., Wang, Liguo, Fraser, D., & Lymburner, L. (2015) Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine. In Dutt, Ashok, Noble, Allen G., Costa, Frank J., Thakur, Sudhir K., Thakur, Rajiv, & Sharma, Hari S. (Eds.) Spatial Diversity and Dynamics in Resources and Urban Development. Springer Netherlands, pp. 37-71.

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The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.

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ID Code: 95562
Item Type: Book Chapter
Additional URLs:
Keywords: Linear Spectral Unmixing, Remote Sensing, Flood mapping, Endmember selection, Extended Support Vector Machine
DOI: 10.1007/978-94-017-9771-9_3
ISBN: 9789401797702
Subjects: Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > PHYSICAL GEOGRAPHY AND ENVIRONMENTAL GEOSCIENCE (040600) > Natural Hazards (040604)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > GEOMATIC ENGINEERING (090900) > Geospatial Information Systems (090903)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > GEOMATIC ENGINEERING (090900) > Photogrammetry and Remote Sensing (090905)
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Springer Netherlands, 2015, Springer Science+Business Media Dordrecht
Deposited On: 15 May 2016 23:47
Last Modified: 28 Nov 2016 07:05

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