Skip to main content
Erschienen in: Neural Processing Letters 3/2019

27.06.2018

Application of Hyperspectral Image Classification Based on Overlap Pooling

verfasst von: Hongmin Gao, Shuo Lin, Chenming Li, Yao Yang

Erschienen in: Neural Processing Letters | Ausgabe 3/2019

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Convolutional neural networks (CNN) are increasingly being used in hyperspectral image (HSI) classification. However, most pooling methods are non-overlap pooling and ignore the influence of neighboring pixels on image characteristics, thereby limiting network classification accuracy. This work presents a deep CNN that is based on overlap pooling; in this model, non-overlap pooling is replaced with overlap pooling to improve the accuracy of feature extraction. However, overlap pooling introduces additional noise while improving feature accuracy. We have found that different combinations of max pooling and mean pooling can effectively solve the problem and significantly improve classification performance. The best pooling combination (max–mean–mean) for HSI classification is obtained after verification through experiments. A rectified linear unit activation function layer and the softmax loss classification model are combined to improve overall classification accuracy. Experiments on three HSI data sets, namely, Indian Pines, Salinas and Pavia University, show that the CNN model can increase overall accuracy to 95.66, 97.8 and 97.48%, respectively. Compared with deep network models such as deep belief network and non-overlap CNN, the proposed model has significantly improved the classification accuracy, and thus verifying the high accuracy of feature extraction of overlap pooling in CNN.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Benediktsson JA, Ghamisi P (2015) Spectral–spatial classification of hyperspectral remote sensing images. Artech House, Boston Benediktsson JA, Ghamisi P (2015) Spectral–spatial classification of hyperspectral remote sensing images. Artech House, Boston
2.
Zurück zum Zitat Du Q, Zhang L, Zhang B, Tong X, Du P, Chanussot J (2013) Foreword to the special issue on hyperspectral remote sensing: theory, methods, and applications. IEEE J Sel Top Appl Earth Observ Remote Sens 6(2):459–465CrossRef Du Q, Zhang L, Zhang B, Tong X, Du P, Chanussot J (2013) Foreword to the special issue on hyperspectral remote sensing: theory, methods, and applications. IEEE J Sel Top Appl Earth Observ Remote Sens 6(2):459–465CrossRef
3.
Zurück zum Zitat Younan N, Aksoy S, King R (2012) Foreword to the special issue on pattern recognition in remote sensing. IEEE J Sel Top Appl Earth Observ Remote Sens 5(5):1331–1334CrossRef Younan N, Aksoy S, King R (2012) Foreword to the special issue on pattern recognition in remote sensing. IEEE J Sel Top Appl Earth Observ Remote Sens 5(5):1331–1334CrossRef
4.
Zurück zum Zitat Chang C-I (2013) Hyperspectral data processing: algorithm design and analysis. Wiley, New YorkCrossRefMATH Chang C-I (2013) Hyperspectral data processing: algorithm design and analysis. Wiley, New YorkCrossRefMATH
5.
Zurück zum Zitat Blanzieri E, Melgani F (2008) Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Trans Geosci Remote Sens 46(6):1804–1811CrossRef Blanzieri E, Melgani F (2008) Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Trans Geosci Remote Sens 46(6):1804–1811CrossRef
6.
Zurück zum Zitat Gao L et al (2015) Subspace-based support vector machines for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12(2):349–353CrossRef Gao L et al (2015) Subspace-based support vector machines for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12(2):349–353CrossRef
7.
Zurück zum Zitat Ma X, Geng J, Wang H (2015) Hyperspectral image classification via contextual deep learning. EURASIP J Image Video Process 20(1):1–12 Ma X, Geng J, Wang H (2015) Hyperspectral image classification via contextual deep learning. EURASIP J Image Video Process 20(1):1–12
8.
Zurück zum Zitat Zabalza J et al (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185(12):1–10CrossRef Zabalza J et al (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185(12):1–10CrossRef
9.
Zurück zum Zitat Ma X, Wang H, Geng J (2016) Spectral–spatial classification of hyperspectral image based on deep auto-encoder. IEEE J Sel Top Appl Earth Obs Remote Sens 99:1–13 Ma X, Wang H, Geng J (2016) Spectral–spatial classification of hyperspectral image based on deep auto-encoder. IEEE J Sel Top Appl Earth Obs Remote Sens 99:1–13
10.
Zurück zum Zitat Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2094–2107CrossRef Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2094–2107CrossRef
11.
Zurück zum Zitat Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2381–2392CrossRef Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2381–2392CrossRef
12.
Zurück zum Zitat LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRef LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRef
13.
Zurück zum Zitat Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), pp 1097–1105 Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), pp 1097–1105
14.
Zurück zum Zitat Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: The IEEE conference on computer vision and pattern recognition (CVPR) Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: The IEEE conference on computer vision and pattern recognition (CVPR)
15.
Zurück zum Zitat Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 248–255 Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 248–255
16.
Zurück zum Zitat He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: The IEEE conference on computer vision and pattern recognition (CVPR) He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: The IEEE conference on computer vision and pattern recognition (CVPR)
17.
Zurück zum Zitat Zhao W, Du S (2016) Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS J Photogramm Remote Sens 113:155–165CrossRef Zhao W, Du S (2016) Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS J Photogramm Remote Sens 113:155–165CrossRef
18.
Zurück zum Zitat Zhao W, Du S (2016) Spectral–spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54(8):4544–4554CrossRef Zhao W, Du S (2016) Spectral–spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54(8):4544–4554CrossRef
19.
Zurück zum Zitat Romero A, Gatta C, Camps-Valls G (2016) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362CrossRef Romero A, Gatta C, Camps-Valls G (2016) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362CrossRef
20.
Zurück zum Zitat Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: Proceedings of IGARSS, pp 4959–4962 Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: Proceedings of IGARSS, pp 4959–4962
21.
Zurück zum Zitat Li W, Wu G, Zhang F et al (2016) Hyperspectral image classification using deep pixel-pair features. IEEE Trans Geosci Remote Sens 55(2):844–853CrossRef Li W, Wu G, Zhang F et al (2016) Hyperspectral image classification using deep pixel-pair features. IEEE Trans Geosci Remote Sens 55(2):844–853CrossRef
22.
Zurück zum Zitat Chen Y, Jiang H, Li C et al (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251CrossRef Chen Y, Jiang H, Li C et al (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251CrossRef
23.
Zurück zum Zitat LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRef LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRef
24.
Zurück zum Zitat Qian Y, Ye M, Zhou J (2012) Hyperspectral image classification based structured sparse logistic regression and three-dimensional wavelet texturefeatures. IEEE Trans Geosci Remote Sens 51(4):2276–2291CrossRef Qian Y, Ye M, Zhou J (2012) Hyperspectral image classification based structured sparse logistic regression and three-dimensional wavelet texturefeatures. IEEE Trans Geosci Remote Sens 51(4):2276–2291CrossRef
25.
Zurück zum Zitat Shen L, Jia S (2011) Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 49(12):5039–5046CrossRef Shen L, Jia S (2011) Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 49(12):5039–5046CrossRef
26.
Zurück zum Zitat Tang YY, Lu Y, Yuan H (2015) Hyperspectral image classification based on three-dimensional scattering wavelet transform. IEEE Trans Geosci Remote Sens 53(5):2467–2480CrossRef Tang YY, Lu Y, Yuan H (2015) Hyperspectral image classification based on three-dimensional scattering wavelet transform. IEEE Trans Geosci Remote Sens 53(5):2467–2480CrossRef
27.
Zurück zum Zitat Li W et al (2015) Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 53(7):3681–3693CrossRef Li W et al (2015) Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 53(7):3681–3693CrossRef
28.
Zurück zum Zitat Waske B, van der Linden S, Benediktsson JA, Rabe A, Hostert P (2010) Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Trans Geosci Remote Sens 48(7):2880–2889CrossRef Waske B, van der Linden S, Benediktsson JA, Rabe A, Hostert P (2010) Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Trans Geosci Remote Sens 48(7):2880–2889CrossRef
30.
Zurück zum Zitat Li W, Wu G, Zhang F, Du Q (2017) Hyperspectral image classification using deep pixel-pair features. IEEE Trans Geosci Remote Sens 55(2):844–853CrossRef Li W, Wu G, Zhang F, Du Q (2017) Hyperspectral image classification using deep pixel-pair features. IEEE Trans Geosci Remote Sens 55(2):844–853CrossRef
31.
Zurück zum Zitat Santara A et al (2016) BASS net: band-adaptive spectral–spatial feature learning neural network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(9):5293–5301CrossRef Santara A et al (2016) BASS net: band-adaptive spectral–spatial feature learning neural network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(9):5293–5301CrossRef
Metadaten
Titel
Application of Hyperspectral Image Classification Based on Overlap Pooling
verfasst von
Hongmin Gao
Shuo Lin
Chenming Li
Yao Yang
Publikationsdatum
27.06.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9876-7

Weitere Artikel der Ausgabe 3/2019

Neural Processing Letters 3/2019 Zur Ausgabe