Modified Nonparametric Weighted Feature Extraction Algorithm

Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spect...
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Gespeichert in:

Autor*in:	Cui, Linlin [verfasserIn] Li, Guosheng [verfasserIn] Ren, Huiru [verfasserIn] He, Lei [verfasserIn] Liao, Huajun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Spectral pan-similarity measure (SPM) Euclidean distance (ED) Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) Nonparametric weighted feature extraction (NWFE)

Übergeordnetes Werk:	Enthalten in: Journal of the Indian Society of Remote Sensing - Neu Delhi : Springer India, 2008, 43(2014), 1 vom: 29. Juli, Seite 69-78
Übergeordnetes Werk:	volume:43 ; year:2014 ; number:1 ; day:29 ; month:07 ; pages:69-78

Links:	Volltext

DOI / URN:	10.1007/s12524-014-0394-4

Katalog-ID:	SPR026020238

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520			\|a Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE’s runtime is slightly longer than that of NWFE.
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650		4	\|a Nonparametric weighted feature extraction (NWFE) \|7 (dpeaa)DE-He213
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912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
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912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
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912			\|a GBV_ILN_2065
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912			\|a GBV_ILN_2093
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912			\|a GBV_ILN_2152
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912			\|a GBV_ILN_2232
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matchkey_str	article:09743006:2014----::oiidoprmtiwihefauex
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allfields	10.1007/s12524-014-0394-4 doi (DE-627)SPR026020238 (SPR)s12524-014-0394-4-e DE-627 ger DE-627 rakwb eng 550 ASE Cui, Linlin verfasserin aut Modified Nonparametric Weighted Feature Extraction Algorithm 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE’s runtime is slightly longer than that of NWFE. Spectral pan-similarity measure (SPM) (dpeaa)DE-He213 Euclidean distance (ED) (dpeaa)DE-He213 Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) (dpeaa)DE-He213 Nonparametric weighted feature extraction (NWFE) (dpeaa)DE-He213 Li, Guosheng verfasserin aut Ren, Huiru verfasserin aut He, Lei verfasserin aut Liao, Huajun verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 43(2014), 1 vom: 29. Juli, Seite 69-78 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:43 year:2014 number:1 day:29 month:07 pages:69-78 https://dx.doi.org/10.1007/s12524-014-0394-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2014 1 29 07 69-78
spelling	10.1007/s12524-014-0394-4 doi (DE-627)SPR026020238 (SPR)s12524-014-0394-4-e DE-627 ger DE-627 rakwb eng 550 ASE Cui, Linlin verfasserin aut Modified Nonparametric Weighted Feature Extraction Algorithm 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE’s runtime is slightly longer than that of NWFE. Spectral pan-similarity measure (SPM) (dpeaa)DE-He213 Euclidean distance (ED) (dpeaa)DE-He213 Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) (dpeaa)DE-He213 Nonparametric weighted feature extraction (NWFE) (dpeaa)DE-He213 Li, Guosheng verfasserin aut Ren, Huiru verfasserin aut He, Lei verfasserin aut Liao, Huajun verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 43(2014), 1 vom: 29. Juli, Seite 69-78 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:43 year:2014 number:1 day:29 month:07 pages:69-78 https://dx.doi.org/10.1007/s12524-014-0394-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2014 1 29 07 69-78
allfields_unstemmed	10.1007/s12524-014-0394-4 doi (DE-627)SPR026020238 (SPR)s12524-014-0394-4-e DE-627 ger DE-627 rakwb eng 550 ASE Cui, Linlin verfasserin aut Modified Nonparametric Weighted Feature Extraction Algorithm 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE’s runtime is slightly longer than that of NWFE. Spectral pan-similarity measure (SPM) (dpeaa)DE-He213 Euclidean distance (ED) (dpeaa)DE-He213 Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) (dpeaa)DE-He213 Nonparametric weighted feature extraction (NWFE) (dpeaa)DE-He213 Li, Guosheng verfasserin aut Ren, Huiru verfasserin aut He, Lei verfasserin aut Liao, Huajun verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 43(2014), 1 vom: 29. Juli, Seite 69-78 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:43 year:2014 number:1 day:29 month:07 pages:69-78 https://dx.doi.org/10.1007/s12524-014-0394-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2014 1 29 07 69-78
allfieldsGer	10.1007/s12524-014-0394-4 doi (DE-627)SPR026020238 (SPR)s12524-014-0394-4-e DE-627 ger DE-627 rakwb eng 550 ASE Cui, Linlin verfasserin aut Modified Nonparametric Weighted Feature Extraction Algorithm 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE’s runtime is slightly longer than that of NWFE. Spectral pan-similarity measure (SPM) (dpeaa)DE-He213 Euclidean distance (ED) (dpeaa)DE-He213 Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) (dpeaa)DE-He213 Nonparametric weighted feature extraction (NWFE) (dpeaa)DE-He213 Li, Guosheng verfasserin aut Ren, Huiru verfasserin aut He, Lei verfasserin aut Liao, Huajun verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 43(2014), 1 vom: 29. Juli, Seite 69-78 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:43 year:2014 number:1 day:29 month:07 pages:69-78 https://dx.doi.org/10.1007/s12524-014-0394-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2014 1 29 07 69-78
allfieldsSound	10.1007/s12524-014-0394-4 doi (DE-627)SPR026020238 (SPR)s12524-014-0394-4-e DE-627 ger DE-627 rakwb eng 550 ASE Cui, Linlin verfasserin aut Modified Nonparametric Weighted Feature Extraction Algorithm 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE’s runtime is slightly longer than that of NWFE. Spectral pan-similarity measure (SPM) (dpeaa)DE-He213 Euclidean distance (ED) (dpeaa)DE-He213 Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) (dpeaa)DE-He213 Nonparametric weighted feature extraction (NWFE) (dpeaa)DE-He213 Li, Guosheng verfasserin aut Ren, Huiru verfasserin aut He, Lei verfasserin aut Liao, Huajun verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 43(2014), 1 vom: 29. Juli, Seite 69-78 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:43 year:2014 number:1 day:29 month:07 pages:69-78 https://dx.doi.org/10.1007/s12524-014-0394-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2014 1 29 07 69-78
language	English
source	Enthalten in Journal of the Indian Society of Remote Sensing 43(2014), 1 vom: 29. Juli, Seite 69-78 volume:43 year:2014 number:1 day:29 month:07 pages:69-78
sourceStr	Enthalten in Journal of the Indian Society of Remote Sensing 43(2014), 1 vom: 29. Juli, Seite 69-78 volume:43 year:2014 number:1 day:29 month:07 pages:69-78
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Spectral pan-similarity measure (SPM) Euclidean distance (ED) Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) Nonparametric weighted feature extraction (NWFE)
dewey-raw	550
isfreeaccess_bool	false
container_title	Journal of the Indian Society of Remote Sensing
authorswithroles_txt_mv	Cui, Linlin @@aut@@ Li, Guosheng @@aut@@ Ren, Huiru @@aut@@ He, Lei @@aut@@ Liao, Huajun @@aut@@
publishDateDaySort_date	2014-07-29T00:00:00Z
hierarchy_top_id	573088853
dewey-sort	3550
id	SPR026020238
language_de	englisch
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author	Cui, Linlin
spellingShingle	Cui, Linlin ddc 550 misc Spectral pan-similarity measure (SPM) misc Euclidean distance (ED) misc Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) misc Nonparametric weighted feature extraction (NWFE) Modified Nonparametric Weighted Feature Extraction Algorithm
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topic_title	550 ASE Modified Nonparametric Weighted Feature Extraction Algorithm Spectral pan-similarity measure (SPM) (dpeaa)DE-He213 Euclidean distance (ED) (dpeaa)DE-He213 Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) (dpeaa)DE-He213 Nonparametric weighted feature extraction (NWFE) (dpeaa)DE-He213
topic	ddc 550 misc Spectral pan-similarity measure (SPM) misc Euclidean distance (ED) misc Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) misc Nonparametric weighted feature extraction (NWFE)
topic_unstemmed	ddc 550 misc Spectral pan-similarity measure (SPM) misc Euclidean distance (ED) misc Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) misc Nonparametric weighted feature extraction (NWFE)
topic_browse	ddc 550 misc Spectral pan-similarity measure (SPM) misc Euclidean distance (ED) misc Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) misc Nonparametric weighted feature extraction (NWFE)
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title	Modified Nonparametric Weighted Feature Extraction Algorithm
ctrlnum	(DE-627)SPR026020238 (SPR)s12524-014-0394-4-e
title_full	Modified Nonparametric Weighted Feature Extraction Algorithm
author_sort	Cui, Linlin
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author_browse	Cui, Linlin Li, Guosheng Ren, Huiru He, Lei Liao, Huajun
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title_sort	modified nonparametric weighted feature extraction algorithm
title_auth	Modified Nonparametric Weighted Feature Extraction Algorithm
abstract	Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE’s runtime is slightly longer than that of NWFE.
abstractGer	Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE’s runtime is slightly longer than that of NWFE.
abstract_unstemmed	Abstract Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE’s runtime is slightly longer than that of NWFE.
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container_issue	1
title_short	Modified Nonparametric Weighted Feature Extraction Algorithm
url	https://dx.doi.org/10.1007/s12524-014-0394-4
remote_bool	true
author2	Li, Guosheng Ren, Huiru He, Lei Liao, Huajun
author2Str	Li, Guosheng Ren, Huiru He, Lei Liao, Huajun
ppnlink	573088853
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isOA_txt	false
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doi_str	10.1007/s12524-014-0394-4
up_date	2024-07-03T18:23:37.743Z
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score	7.4005327

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Modified Nonparametric Weighted Feature Extraction Algorithm

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