Deep Filter Banks for Land-Use Scene Classification
Land-use (LU) scene classification is one of the most challenging tasks in the field of remote sensing (RS) image processing due to its high intraclass variability and low interclass distance. Motivated by the challenge posed by this problem, we propose a novel hybrid architecture, deep filter banks...
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| Vydáno v: | IEEE geoscience and remote sensing letters Ročník 13; číslo 12; s. 1895 - 1899 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
IEEE
01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1545-598X, 1558-0571 |
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| Abstract | Land-use (LU) scene classification is one of the most challenging tasks in the field of remote sensing (RS) image processing due to its high intraclass variability and low interclass distance. Motivated by the challenge posed by this problem, we propose a novel hybrid architecture, deep filter banks, combining multicolumn stacked denoising sparse autoencoder (SDSAE) and Fisher vector (FV) to automatically learn the representative and discriminative features in a hierarchical manner for LU scene classification. SDSAE kernels describe local patches and a robust global feature of the RS image is built through the FV pooling layer. Unlike previous handcrafted features, we use machine-learning mechanisms to optimize our proposed feature extractor so that it can learn more suitable internal features from the RS data, boosting the final performance. Our approach achieves superior performance compared with the state-of-the-art methods, obtaining average classification accuracies of 92.7% and 90.4%, respectively, on the UC Merced and RSSCN7 data sets. |
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| AbstractList | Land-use (LU) scene classification is one of the most challenging tasks in the field of remote sensing (RS) image processing due to its high intraclass variability and low interclass distance. Motivated by the challenge posed by this problem, we propose a novel hybrid architecture, deep filter banks, combining multicolumn stacked denoising sparse autoencoder (SDSAE) and Fisher vector (FV) to automatically learn the representative and discriminative features in a hierarchical manner for LU scene classification. SDSAE kernels describe local patches and a robust global feature of the RS image is built through the FV pooling layer. Unlike previous handcrafted features, we use machine-learning mechanisms to optimize our proposed feature extractor so that it can learn more suitable internal features from the RS data, boosting the final performance. Our approach achieves superior performance compared with the state-of-the-art methods, obtaining average classification accuracies of 92.7% and 90.4%, respectively, on the UC Merced and RSSCN7 data sets. |
| Author | Hang Wu Baozhen Liu Weihua Su Jinggong Sun Wenchang Zhang |
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| References_xml | – volume: 35 start-page: 2296 year: 2014 ident: ref4 article-title: A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification publication-title: Int J Remote Sens doi: 10.1080/01431161.2014.890762 – ident: ref8 doi: 10.3390/rs8050436 – ident: ref3 doi: 10.1145/1869790.1869829 – ident: ref7 doi: 10.1109/TGRS.2014.2351395 – ident: ref23 doi: 10.1109/LGRS.2015.2475299 – ident: ref17 doi: 10.1038/nature14539 – ident: ref18 doi: 10.1109/TGRS.2013.2241444 – ident: ref19 doi: 10.1109/TGRS.2014.2357078 – ident: ref5 doi: 10.1007/978-3-642-33709-3_52 – ident: ref10 doi: 10.1007/s11760-015-0804-2 – ident: ref21 doi: 10.1117/1.JRS.10.025006 – ident: ref6 doi: 10.1109/ICCV.2011.6126403 – volume: 11 start-page: 3371 year: 2010 ident: ref13 article-title: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion publication-title: J Mach Learn Res – ident: ref20 doi: 10.1007/978-3-642-39402-7_33 – start-page: 1097 year: 2012 ident: ref14 article-title: Imagenet classification with deep convolutional neural networks publication-title: Advances in neural information processing systems – ident: ref9 doi: 10.1109/LGRS.2010.2055033 – ident: ref11 doi: 10.1109/CVPR.2015.7298998 – ident: ref16 doi: 10.1109/TGRS.2015.2480866 – ident: ref22 doi: 10.1109/JSTARS.2015.2444405 – ident: ref1 doi: 10.1109/MGRS.2016.2540798 – ident: ref2 doi: 10.1016/j.isprsjprs.2016.03.004 – ident: ref15 doi: 10.1109/TNNLS.2015.2477537 – volume: 313 start-page: 504 year: 2006 ident: ref12 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 |
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| SubjectTerms | Classification Data acquisition Data models Deep filter banks Encoding Feature extraction Filter banks Filters Fisher vector (FV) Image processing Kernel Land use land-use (LU) scene classification Learning algorithms Machine learning Noise reduction Remote sensing Robustness Semantics stacked denoising sparse autoencoder (SDSAE) |
| Title | Deep Filter Banks for Land-Use Scene Classification |
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