Abstract
The most difficult operation in 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 of the presence of mixed pixels in the images. To solve the mixed pixel problem, advanced image processing techniques are adopted, and the linear spectral unmixing method is one of the most popular soft classification techniques used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues: the method of selecting endmembers and the method to model the endmembers for unmixing. This chapter presents an improvement in the adaptive selection of the endmember subset for each pixel in the spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause overestimation of the endmember spectra residing in a mixed pixel and hence reduce the performance level of spectral unmixing. Compared to this, application of an estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this chapter, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and is 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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Dey Sarker, C., Jia, X., Wang, L., Fraser, D., Lymburner, L. (2015). Spectral Unmixing with Estimated Adaptive Endmember Index Using Extended Support Vector Machine. In: Dutt, A., Noble, A., Costa, F., Thakur, S., Thakur, R., Sharma, H. (eds) Spatial Diversity and Dynamics in Resources and Urban Development. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9771-9_3
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DOI: https://doi.org/10.1007/978-94-017-9771-9_3
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