A Comparative Study for Hyperspectral Data Classification with Deep Learning and Dimensionality Reduction Techniques

Yıl 2018, Cilt: 23 Sayı: 3, 73 - 90, 26.10.2018

Gizem Ortaç , Gıyasettin Özcan

https://doi.org/10.17482/uumfd.435723

Öz

In recent years, hyperspectral imaging has been a
popular subject in the remote sensing community by providing a rich amount of
information for each pixel about fields. In general, dimensionality reduction
techniques are utilized before classification in statistical
pattern-classification to handle high-dimensional and highly correlated feature
spaces. However, traditional classifiers and dimensionality reduction methods
are difficult tasks in the spectral domain and cannot extract discriminative
features. Recently, deep convolutional neural networks are proposed to classify
hyperspectral images directly in the spectral domain. In this paper, we present
comparative study among traditional data reduction techniques and convolutional
neural network. The obtained results on hyperspectral image
data sets show that our proposed CNN architecture improves
the accuracy rates for classification performance, when compared to traditional
methods by increasing the classification accuracy rate by 3% and 6%. 

Anahtar Kelimeler

Hyperspectral Imaging, Deep Learning, Dimensionality Reduction, Classification, Convolutional Neural Networks

HİPERSPEKTRAL VERİLERİN SINIFLANDIRMASINDA DERİN ÖĞRENME VE BOYUT İNDİRGEME TEKNİKLERİNİN KARŞILAŞTIRILMASI

Öz

Son yıllarda, hiperspektral görüntüleme yüzey
pikselleri ile ilgili zengin miktarda bilgi sağlamasıyla uzaktan algılama
alanında popüler bir konu olmuştur. Genel olarak, elde edilen yüksek boyutlu ve
ilişkisel veriyi işlemek için, sınıflandırmadan önce boyut indirgeme teknikleri
uygulanmaktadır. Bununla birlikte geleneksel sınıflandırıcılar ve boyut azaltma
yöntemleri, spektral alanda hala zorlu bir işlemdir ve ayırt edici öznitelikler
çıkarmaz. Son zamanlarda ise derin konvolüsyonel sinir ağları, hiperspektral
görüntüleri doğrudan spektral alanda sınıflandırmak için geliştirilmiştir.
Önerilen çalışmada, geleneksel sınıflandırma ve konvolüsyonel sinir ağları
arasında karşılaştırmalı bir çalışma ve analiz yapılmıştır. Çeşitli
hiperspektral görüntü verilerine dayanarak elde edilen sonuçlar, önerilen
konvolüsyonel sinir ağının, geleneksel yöntemlerden %3 ve %6 oranında daha iyi
bir sınıflandırma oranı sağladığını göstermiştir.

Anahtar Kelimeler

Hiperspektral Görüntüleme, Derin Öğrenme, Boyut Azaltma, Sınıflandırma, Konvolüsyonel Sinir Ağları, Konvolüsyonel Sinir Ağları

Kaynakça

• Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Kudlur, M. (2016, November). TensorFlow: A System for Large-Scale Machine Learning. In OSDI (Vol. 16, pp. 265-283).
• Agarwal, A., El-Ghazawi, T., El-Askary, H., & Le-Moigne, J. (2007, December). Efficient hierarchical-PCA dimension reduction for hyperspectral imagery. In Signal Processing and Information Technology, 2007 IEEE International Symposium on (pp. 353-356). IEEE. DOI: 10.1109/ISSPIT.2007.4458191
• Bandos, T. V., Bruzzone, L., & Camps-Valls, G. (2009). Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 862-873. DOI: 10.1109/TGRS.2008.2005729
• Bartholomew, D. J., Steele, F., Galbraith, J., & Moustaki, I. (2008). Analysis of multivariate social science data. Chapman and Hall/CRC.
• Bazi, Y., & Melgani, F. (2006). Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44(11), 3374-3385. DOI: 10.1109/TGRS.2006.880628.
• Bruce, L. M., Koger, C. H., & Li, J. (2002). Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE Transactions on geoscience and remote sensing, 40(10), 2331-2338. DOI: 10.1109/TGRS.2002.804721.
• Bruna, J., Sprechmann, P., & LeCun, Y. (2015). Super-resolution with deep convolutional sufficient statistics. arXiv preprint arXiv:1511.05666.
• Camps-Valls, G., Gómez-Chova, L., Calpe-Maravilla, J., Martín-Guerrero, J. D., Soria-Olivas, E., Alonso-Chordá, L., & Moreno, J. (2004). Robust support vector method for hyperspectral data classification and knowledge discovery. IEEE Transactions on Geoscience and Remote sensing, 42(7), 1530-1542. DOI: 10.1109/TGRS.2004.827262.
• Camps-Valls, G., Gomez-Chova, L., Muñoz-Marí, J., Vila-Francés, J., & Calpe-Maravilla, J. (2006). Composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 3(1), 93-97. DOI: 10.1109/LGRS.2005.857031.
• Chang, C. I. (2003). Hyperspectral imaging: techniques for spectral detection and classification (Vol. 1). Springer Science & Business Media.
• Chang, C. I., & Du, Q. (1999). Interference and noise-adjusted principal components analysis. IEEE transactions on geoscience and remote sensing, 37(5), 2387-2396. DOI: 10.1109/36.789637. DOI: 10.1109/36.789637.
• Chen, S., & Zhang, D. (2011). Semisupervised dimensionality reduction with pairwise constraints for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 8(2), 369-373. DOI: 10.1109/LGRS.2010.2076407.
• Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016). Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 6232-6251. DOI: 10.1109/TGRS.2016.2584107. DOI: 10.1109/TGRS.2016.2584107
• Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction L. O. Jimenez, D. A. Landgrebe (Nov. 1999) Hyperspectral data analysis and supervised feature reduction via projection pursuit", IEEE Trans. Geosci. Remote Sens., vol. 37, no. 6, pp. 2653-2667. DOI: 10.1109/TGRS.2002.804721.
• Fang, L., Li, S., Kang, X., & Benediktsson, J. A. (2014). Spectral–spatial hyperspectral image classification via multiscale adaptive sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 52(12), 7738-7749. DOI: 10.1109/TGRS.2014.2318058.
• Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on geoscience and remote sensing, 42(6), 1335-1343. DOI: 10.1109/TGRS.2004.827257.
• Fotiadou, K., Tsagkatakis, G., & Tsakalides, P. (2017). Deep Convolutional Neural Networks for the Classification of Snapshot Mosaic Hyperspectral Imagery. Electronic Imaging, 2017(17), 185-190. DOI: https://doi.org/10.2352/ISSN.2470-1173.2017.17.COIMG-445.
• Fukunaga, K. (2013). Introduction to statistical pattern recognition. Academic press.
• Gamba, P. (2004, September). A collection of data for urban area characterization. In Geoscience and Remote Sensing Symposium, 2004. IGARSS'04. Proceedings. 2004 IEEE International (Vol. 1). IEEE. DOI: 10.1109/IGARSS.2004.1368947.
• Girshick, R. (2015) Fast R-CNN. In Proceedings of the International Conference on Computer Vision, Santiago, Chile,; pp. 1440–1448.
• Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
• Glorot, X., & Bengio, Y. (2010, March). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249-256).
• Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.
• Gualtieri, J. A., & Chettri, S. (2000). Support vector machines for classification of hyperspectral data. In Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International (Vol. 2, pp. 813-815). IEEE. DOI: 10.1109/IGARSS.2000.861712
• Halko, N., Martinsson, P. G., & Tropp, J. A. (2011). Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM review, 53(2), 217-288. DOI: https://doi.org/10.1137/090771806.
• He, K., Zhang, X., Ren, S., & Sun, J. (2014, September). Spatial pyramid pooling in deep convolutional networks for visual recognition. In european conference on computer vision (pp. 346-361). Springer, Cham. DOI: 10.1109/TPAMI.2015.2389824.
• Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507. DOI: 10.1126/science.1127647.
• Hoffbeck, J. P., & Landgrebe, D. A. (1996). Classification of remote sensing images having high spectral resolution. Remote Sensing of Environment, 57(3), 119-126.
• Hoffbeck, J. P., & Landgrebe, D. A. (1996). Covariance matrix estimation and classification with limited training data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7), 763-767. DOI: 10.1109/34.506799.
• http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes, Date of Access: 01.06.2018, Topic: Hyperspectral Remote Sensing Scenes
• Hu, W., Huang, Y., Wei, L., Zhang, F., & Li, H. (2015). Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors, 2015. DOI: http://dx.doi.org/10.1155/2015/258619
• Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of remote sensing, 23(4), 725-749. DOI: https://doi.org/10.1080/01431160110040323
• Huang, X., & Zhang, L. (2009). A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia City, northern Italy. International Journal of Remote Sensing, 30(12), 3205-3221. DOI: https://doi.org/10.1080/01431160802559046
• Hughes, G. (1968). On the mean accuracy of statistical pattern recognizers. IEEE transactions on information theory, 14(1), 55-63. DOI: 10.1109/TIT.1968.1054102.
• Jackson, Q., & Landgrebe, D. A. (2001). An adaptive classifier design for high-dimensional data analysis with a limited training data set. IEEE Transactions on Geoscience and Remote Sensing, 39(12), 2664-2679. DOI: 10.1109/36.975001.
• Jimenez, L. O., & Landgrebe, D. A. (1999). Hyperspectral data analysis and supervised feature reduction via projection pursuit. IEEE Transactions on Geoscience and Remote Sensing, 37(6), 2653-2667. DOI: 10.1109/36.803413.
• Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
• Landgrebe, D. A. (2005). Signal theory methods in multispectral remote sensing (Vol. 29). John Wiley & Sons.
• Lee, C., & Landgrebe, D. A. (1993). Feature extraction based on decision boundaries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(4), 388-400. DOI: 10.1109/34.206958.
• Li, H. (2014). Deep learning for image denoising. International Journal of Signal Processing, Image Processing and Pattern Recognition, 7(3), 171-180. DOI: http://dx.doi.org/10.14257/ijsip.2014.7.3.14
• Liang, H., & Li, Q. (2016). Hyperspectral imagery classification using sparse representations of convolutional neural network features. Remote Sensing, 8(2), 99. DOI:10.3390/rs8020099.
• Licciardi, G., Marpu, P. R., Chanussot, J., & Benediktsson, J. A. (2012). Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters, 9(3), 447-451. DOI: 10.1109/LGRS.2011.2172185.
• Liu, F., Shen, C., & Lin, G. (2015). Deep convolutional neural fields for depth estimation from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5162-5170).
• Makantasis, K., Karantzalos, K., Doulamis, A., & Doulamis, N. (2015, July). Deep supervised learning for hyperspectral data classification through convolutional neural networks. In Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International (pp. 4959-4962). IEEE. DOI: 10.1109/IGARSS.2015.7326945.
• Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on geoscience and remote sensing, 42(8), 1778-1790. DOI: 10.1109/TGRS.2004.831865.
• Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807-814).
• P. F. Hsieh (1998) D. Landgrebe, Classification of high dimensional data.
• Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007-1011. DOI: https://doi.org/10.1080/01431160512331314083
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.
• Rodarmel, C., & Shan, J. (2002). Principal component analysis for hyperspectral image classification. Surveying and Land Information Science, 62(2), 115.
• Scott, D. W. (2015). Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons.
• Shahshahani, B. M., & Landgrebe, D. A. (1994). The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE Transactions on Geoscience and remote sensing, 32(5), 1087-1095. DOI: 10.1109/36.312897.
• Sun, Y., Chen, Y., Wang, X., & Tang, X. (2014). Deep learning face representation by joint identification-verification. In Advances in neural information processing systems (pp. 1988-1996).
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015, June). Going deeper with convolutions. Cvpr.
• Tadjudin, S., & Landgrebe, D. A. (1999). Covariance estimation with limited training samples. IEEE Transactions on Geoscience and Remote Sensing, 37(4), 2113-2118.
• Tsai, F., & Philpot, W. D. (2002). A derivative-aided hyperspectral image analysis system for land-cover classification. IEEE Transactions on Geoscience and Remote Sensing, 40(2), 416-425. DOI: 10.1109/36.774728.
• Tuia, D., Volpi, M., Dalla Mura, M., Rakotomamonjy, A., & Flamary, R. (2014). Automatic feature learning for spatio-spectral image classification with sparse SVM. IEEE Transactions on Geoscience and Remote Sensing, 52(10), 6062-6074. DOI: 10.1109/TGRS.2013.2294724.
• Villa, A., Benediktsson, J. A., Chanussot, J., & Jutten, C. (2011). Hyperspectral image classification with independent component discriminant analysis. IEEE transactions on Geoscience and remote sensing, 49(12), 4865-4876. DOI: 10.1109/TGRS.2011.2153861.
• Wall, M. E., Rechtsteiner, A., & Rocha, L. M. (2003). Singular value decomposition and principal component analysis. In A practical approach to microarray data analysis (pp. 91-109). Springer, Boston, MA.
• Wang, S., & Wang, C. (2015). Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 159. DOI:10.5194/isprsarchives-XL-7-W4-159-2015.
• Wang, Y., Lv, Y., Liu, H., Wei, Y., Zhang, J., An, D., & Wu, J. (2018). Identification of maize haploid kernels based on hyperspectral imaging technology. Computers and Electronics in Agriculture, 153, 188-195. DOI: https://doi.org/10.1016/j.compag.2018.08.012.
• Xu, C., Lu, C., Gao, J., Zheng, W., Wang, T., & Yan, S. (2015). Discriminative analysis for symmetric positive definite matrices on lie groups. IEEE Transactions on Circuits and Systems for Video Technology, 25(10), 1576-1585. DOI: 10.1109/TCSVT.2015.2392472.
• Yu, S., Jia, S., & Xu, C. (2017). Convolutional neural networks for hyperspectral image classification. Neurocomputing, 219, 88-98. DOI: https://doi.org/10.1016/j.neucom.2016.09.010