Study of soft classification approaches for identification of earthquake-induced liquefied soil
| Autor: | H. R. Wason, Y. V. N. Krishna Murthy, Sanjay Ghosh, Sandeep Singh Sengar, P. L. N. Raju, Anil Kumar |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Soft computing
lcsh:GE1-350 Pixel Soft classification lcsh:Risk in industry. Risk management Spectral bands Land cover Fuzzy logic lcsh:TD1-1066 lcsh:HD61 Geography Noise cluster General Earth and Planetary Sciences lcsh:Environmental technology. Sanitary engineering Soil liquefaction lcsh:Environmental sciences General Environmental Science Remote sensing |
| Zdroj: | Geomatics, Natural Hazards & Risk, Vol 5, Iss 4, Pp 334-352 (2014) |
| ISSN: | 1947-5713 1947-5705 |
| Popis: | The existence of mixed pixels led to the development of several approaches for soft (or fuzzy) classification in which each pixel is allocated to all classes in varying proportions. However, while the proportions of each land cover within each pixel may be predicted, the spatial location of each land cover within each pixel is not. There exist many different potential techniques for sub-pixel mapping from remotely sensed imagery to identify specific class. The fuzzy-based possibilistic c-means (PCM), noise cluster (NC) and noise cluster with entropy (NCE) classifiers were applied to identify the Bhuj, India (2001), earthquake induced soil liquefaction and compared as soft computing approaches via supervised classification. The soil liquefaction identification was empirically investigated and compared with class-based sensor-independent (CBSI) spectral band ratio using Landsat-7 temporal images. It has been found that CBSI-based temporal indices yield the better results for identification of liquefied soil areas while it was easily separated with pre-earthquake existing water body in that area. The NCE classifier performed better for conventional temporal indices, while NC classifier performed better for soil liquefaction and PCM classifier performed better for water body identification with CBSI temporal indices. |
| Databáze: | OpenAIRE |
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