Dimensionality Reduction of Hyperspectral Images Using Pooling

Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Paul, Arati [verfasserIn] Chaki, Nabendu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	hyperspectral pooling dimensionality reduction classification

Anmerkung:	© Pleiades Publishing, Ltd. 2019

Übergeordnetes Werk:	Enthalten in: Pattern recognition and image analysis - Moscow : MAIK Nauka/Interperiodica Publ., 1996, 29(2019), 1 vom: Jan., Seite 72-78
Übergeordnetes Werk:	volume:29 ; year:2019 ; number:1 ; month:01 ; pages:72-78

Links:	Volltext

DOI / URN:	10.1134/S1054661819010085

Katalog-ID:	SPR020183380

Internformat


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520			\|a Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time.
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912			\|a GBV_ILN_40
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912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
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_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_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
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912			\|a GBV_ILN_2007
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912			\|a GBV_ILN_2011
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912			\|a GBV_ILN_2021
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912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
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912			\|a GBV_ILN_2057
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912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
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912			\|a GBV_ILN_2144
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912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
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912			\|a GBV_ILN_4336
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Indexfelder

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matchkey_str	article:15556212:2019----::iesoaiyeutoohprpcrlmg
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publishDate	2019
allfields	10.1134/S1054661819010085 doi (DE-627)SPR020183380 (SPR)S1054661819010085-e DE-627 ger DE-627 rakwb eng Paul, Arati verfasserin aut Dimensionality Reduction of Hyperspectral Images Using Pooling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time. hyperspectral (dpeaa)DE-He213 pooling (dpeaa)DE-He213 dimensionality reduction (dpeaa)DE-He213 classification (dpeaa)DE-He213 Chaki, Nabendu aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 29(2019), 1 vom: Jan., Seite 72-78 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:29 year:2019 number:1 month:01 pages:72-78 https://dx.doi.org/10.1134/S1054661819010085 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 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_2472 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 29 2019 1 01 72-78
spelling	10.1134/S1054661819010085 doi (DE-627)SPR020183380 (SPR)S1054661819010085-e DE-627 ger DE-627 rakwb eng Paul, Arati verfasserin aut Dimensionality Reduction of Hyperspectral Images Using Pooling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time. hyperspectral (dpeaa)DE-He213 pooling (dpeaa)DE-He213 dimensionality reduction (dpeaa)DE-He213 classification (dpeaa)DE-He213 Chaki, Nabendu aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 29(2019), 1 vom: Jan., Seite 72-78 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:29 year:2019 number:1 month:01 pages:72-78 https://dx.doi.org/10.1134/S1054661819010085 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 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_2472 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 29 2019 1 01 72-78
allfields_unstemmed	10.1134/S1054661819010085 doi (DE-627)SPR020183380 (SPR)S1054661819010085-e DE-627 ger DE-627 rakwb eng Paul, Arati verfasserin aut Dimensionality Reduction of Hyperspectral Images Using Pooling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time. hyperspectral (dpeaa)DE-He213 pooling (dpeaa)DE-He213 dimensionality reduction (dpeaa)DE-He213 classification (dpeaa)DE-He213 Chaki, Nabendu aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 29(2019), 1 vom: Jan., Seite 72-78 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:29 year:2019 number:1 month:01 pages:72-78 https://dx.doi.org/10.1134/S1054661819010085 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 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_2472 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 29 2019 1 01 72-78
allfieldsGer	10.1134/S1054661819010085 doi (DE-627)SPR020183380 (SPR)S1054661819010085-e DE-627 ger DE-627 rakwb eng Paul, Arati verfasserin aut Dimensionality Reduction of Hyperspectral Images Using Pooling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time. hyperspectral (dpeaa)DE-He213 pooling (dpeaa)DE-He213 dimensionality reduction (dpeaa)DE-He213 classification (dpeaa)DE-He213 Chaki, Nabendu aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 29(2019), 1 vom: Jan., Seite 72-78 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:29 year:2019 number:1 month:01 pages:72-78 https://dx.doi.org/10.1134/S1054661819010085 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 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_2472 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 29 2019 1 01 72-78
allfieldsSound	10.1134/S1054661819010085 doi (DE-627)SPR020183380 (SPR)S1054661819010085-e DE-627 ger DE-627 rakwb eng Paul, Arati verfasserin aut Dimensionality Reduction of Hyperspectral Images Using Pooling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time. hyperspectral (dpeaa)DE-He213 pooling (dpeaa)DE-He213 dimensionality reduction (dpeaa)DE-He213 classification (dpeaa)DE-He213 Chaki, Nabendu aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 29(2019), 1 vom: Jan., Seite 72-78 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:29 year:2019 number:1 month:01 pages:72-78 https://dx.doi.org/10.1134/S1054661819010085 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 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_2472 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 29 2019 1 01 72-78
language	English
source	Enthalten in Pattern recognition and image analysis 29(2019), 1 vom: Jan., Seite 72-78 volume:29 year:2019 number:1 month:01 pages:72-78
sourceStr	Enthalten in Pattern recognition and image analysis 29(2019), 1 vom: Jan., Seite 72-78 volume:29 year:2019 number:1 month:01 pages:72-78
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	hyperspectral pooling dimensionality reduction classification
isfreeaccess_bool	false
container_title	Pattern recognition and image analysis
authorswithroles_txt_mv	Paul, Arati @@aut@@ Chaki, Nabendu @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	327148179
id	SPR020183380
language_de	englisch
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author	Paul, Arati
spellingShingle	Paul, Arati misc hyperspectral misc pooling misc dimensionality reduction misc classification Dimensionality Reduction of Hyperspectral Images Using Pooling
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topic_title	Dimensionality Reduction of Hyperspectral Images Using Pooling hyperspectral (dpeaa)DE-He213 pooling (dpeaa)DE-He213 dimensionality reduction (dpeaa)DE-He213 classification (dpeaa)DE-He213
topic	misc hyperspectral misc pooling misc dimensionality reduction misc classification
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title	Dimensionality Reduction of Hyperspectral Images Using Pooling
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title_full	Dimensionality Reduction of Hyperspectral Images Using Pooling
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title_sort	dimensionality reduction of hyperspectral images using pooling
title_auth	Dimensionality Reduction of Hyperspectral Images Using Pooling
abstract	Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time. © Pleiades Publishing, Ltd. 2019
abstractGer	Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time. © Pleiades Publishing, Ltd. 2019
abstract_unstemmed	Abstract Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time. © Pleiades Publishing, Ltd. 2019
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title_short	Dimensionality Reduction of Hyperspectral Images Using Pooling
url	https://dx.doi.org/10.1134/S1054661819010085
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up_date	2024-07-03T14:24:53.858Z
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Dimensionality Reduction of Hyperspectral Images Using Pooling

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