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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Published in:	Geomatics, natural hazards and risk Vol. 5; no. 4; pp. 334 - 352
Main Authors:	Sengar, Sandeep Singh, Kumar, Anil, Wason, Hans Raj, Ghosh, Sanjay Kumar, Murthy, Y.V.N. Krishna, Raju, P.L.N.
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 02.10.2014 Taylor & Francis Ltd Taylor & Francis Group
Subjects:	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.
Author_xml	– sequence: 1 givenname: Sandeep Singh surname: Sengar fullname: Sengar, Sandeep Singh email: talktosengar@yahoo.co.in organization: Earthquake Engineering Department, Indian Institute of Technology Roorkee – sequence: 2 givenname: Anil surname: Kumar fullname: Kumar, Anil organization: Indian Institute of Remote Sensing, Indian Space Research Organization – sequence: 3 givenname: Hans Raj surname: Wason fullname: Wason, Hans Raj organization: Earthquake Engineering Department, Indian Institute of Technology Roorkee – sequence: 4 givenname: Sanjay Kumar surname: Ghosh fullname: Ghosh, Sanjay Kumar organization: Civil Engineering Department, Indian Institute of Technology Roorkee – sequence: 5 givenname: Y.V.N. Krishna surname: Murthy fullname: Murthy, Y.V.N. Krishna organization: Indian Institute of Remote Sensing, Indian Space Research Organization – sequence: 6 givenname: P.L.N. surname: Raju fullname: Raju, P.L.N. organization: Indian Institute of Remote Sensing, Indian Space Research Organization
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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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