Study of soft classification approaches for identification of earthquake-induced liquefied soil

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 lan...

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Vydáno v:Geomatics, natural hazards and risk Ročník 5; číslo 4; s. 334 - 352
Hlavní autoři: Sengar, Sandeep Singh, Kumar, Anil, Wason, Hans Raj, Ghosh, Sanjay Kumar, Murthy, Y.V.N. Krishna, Raju, P.L.N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 02.10.2014
Taylor & Francis Ltd
Taylor & Francis Group
Témata:
Classification
Classifiers
Clusters
Earthquakes
Landsat
Liquefaction
Liquefied
Pixels
Remote sensing
Seismic activity
Soil (material)
Soils
Temporal logic
Water bodies
ISSN:1947-5705, 1947-5713
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Abstract 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.
AbstractList 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.
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. [PUBLICATION ABSTRACT]
Author Kumar, Anil
Wason, Hans Raj
Ghosh, Sanjay Kumar
Sengar, Sandeep Singh
Murthy, Y.V.N. Krishna
Raju, P.L.N.
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SubjectTerms Classification
Classifiers
Clusters
Earthquakes
Landsat
Liquefaction
Liquefied
Pixels
Remote sensing
Seismic activity
Soil (material)
Soils
Temporal logic
Water bodies
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Title Study of soft classification approaches for identification of earthquake-induced liquefied soil
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