Kazım HANBAY

Evrişimsel sinir ağı ve iki-boyutlu karmaşık gabor dönüşümü kullanılarak hiperspektral görüntü sınıflandırma

Bu çalışmada 2-boyutlu karmaşık Gabor filtreleme ve derin evrişimsel sinir ağları kullanılarak yeni bir hiperspektral görüntü sınıflandırma yöntemi önerilmiştir. Derin öğrenilen ve Gabor özellik çıkarma metodolojileri giriş hiperspekral örnekler üzerinde eş zamanlı olarak gerçekleştirilmiştir. Görüntülerin Gabor özellikleri çoklu yönelim ve frekanslarda hesaplanır. Sonra derin özellikler ve Gabor özellikleri daha güçlü ve ayırt edici özellik vektörü elde etmek için birleştirilir. Hibrit özellik vektörü hiperspektral görüntü sınıflandırmak için softmax sınıflandırıcıya giriş olarak kullanılır. İki hiperspektral veri seti üzerinde gerçekleştirilen deneyler önerilen yöntemin bazı geleneksel yöntemlerden daha iyi sınıflandırma performansı elde edebildiğini göstermiştir.

Anahtar Kelimeler:

Hiperspektral görüntü sınıflandırma, derin öğrenme

PDF

___

1 Kang, X., Zhang, X., Li, S., Li, K., Li, J., Benediktsson, J.A., Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters, IEEE Trans. Geosci. Remote Sens., 55 (10), 5600–5611, 2017.
2 Lanthier, Y., Bannari, A., Haboudane, D., Miller, J.R., Tremblay, N., Hyperspectral Data Segmentation and Classification in Precision Agriculture: A Multi-Scale Analysis, IEEE International Geoscience and Remote Sensing Symposium, 585-588, 2008.
3 Hörig, B., Kühn, F., Oschütz, F., Lehmann, F., HyMap hyperspectral remote sensing to detect hydrocarbons, Int. J. Remote Sens., 22 (8), 1413–1422, 2001.
4 Blanzieri, E., Melgani, F., Nearest Neighbor Classification of Remote Sensing Images With the Maximal Margin Principle, IEEE Trans. Geosci. Remote Sens., 46 (6), 1804–1811, 2008.
5 Melgani, F., Bruzzone, L., Classification of hyperspectral remote sensing images with support vector machines, IEEE Trans. Geosci. Remote Sens., 42 (8), 1778–1790, 2004.
6 Ratle, F., Camps-Valls, G., Weston, J., Semisupervised Neural Networks for Efficient Hyperspectral Image Classification, IEEE Trans. Geosci. Remote Sens., 48 (5), 2271–2282, 2010.
7 Kang, X., Li, S., Fang, L., Benediktsson, J.A., Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images, IEEE Trans. Geosci. Remote Sens., 53 (4), 2241–2253, 2015.
8 Prasad, S., Bruce, L.M., Limitations of Principal Components Analysis for Hyperspectral Target Recognition, IEEE Geosci. Remote Sens. Lett., , 5 (4), 625–629, 2008.
9 Tarabalka, Y., Fauvel, M., Chanussot, J., Benediktsson, J.A., SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images, IEEE Geosci. Remote Sens. Lett., 7 (4), 736–740, 2010.
10 Li, L., Wang, C., Li, W., Chen, J., Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines, Neurocomputing, 275, 1725–1733, 2018.
11 Li, W., Du, Q., Zhang, F., Hu, W., Hyperspectral Image Classification by Fusing Collaborative and Sparse Representations, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9 (9), 4178–4187, 2016.
12 Debba, P., van Ruitenbeek, F.J.A., van der Meer, F.D., Carranza, E.J.M., Stein, A., Optimal field sampling for targeting minerals using hyperspectral data, Remote Sens. Environ., 99 (4), 373–386, 2005.
13 Li, Y., Xie, W., Li, H., Hyperspectral image reconstruction by deep convolutional neural network for classification, Pattern Recognit., 63, 371–383, 2016.
14 Yu, S., Jia, S., Xu, C., Convolutional neural networks for hyperspectral image classification, Neurocomputing, 219 (5), 88–98, 2017.
15 Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y., Deep Learning-Based Classification of Hyperspectral Data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7 (6), 2094–2107, 2014.
16 Hu, W., Huang, Y., Wei, L., Zhang, F., Li, H., Deep Convolutional Neural Networks for Hyperspectral Image Classification, J. Sensors, 2015, 1–12, 2015.
17 Zabalza, J., Ren, J., Zheng, J., et al., Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging, Neurocomputing, 185, 1–10, 2016.
18 Li, W., Wu, G., Zhang, F., Du, Q., Hyperspectral Image Classification Using Deep Pixel-Pair Features, IEEE Trans. Geosci. Remote Sens., 55 (2), 844–853, 2017.
19 Kang, X., Li, C., Li, S., Lin, H., Classification of Hyperspectral Images by Gabor Filtering Based Deep Network, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 11 (4), 1166–1178, 2018.
20 Shi, C., Pun, C.-M., 3D multi-resolution wavelet convolutional neural networks for hyperspectral image classification, Inf. Sci., 420, 49–65, 2017.
21 Cai, L., Zhu, J., Zeng, H., Chen, J., Cai, C., Ma, K.-K., HOG-assisted deep feature learning for pedestrian gender recognition, J. Franklin Inst., 355 (4), 1991–2008, 2018.
22 Altun, A.A., Allahverdi, N., A new approach to recognition of fingerprints enhanced by filtering techniques with artifıcial neural networks, J. Fac. Eng. Archit. Gazi Univ., 22 (2), 227–236, 2007.
23 Kim, J., Um, S., Min, D., Fast 2D Complex Gabor Filter With Kernel Decomposition, IEEE Trans. Image Process., 27 (4), 1713–1722, 2018.
24 Bernardino, A., Santos-Victor, J., Fast IIR Isotropic 2-D Complex Gabor Filters With Boundary Initialization, IEEE Trans. Image Process., 15 (11), 3338–3348, 2006.
25 Kavitha, K., Arivazhagan, S. Fuzzy inspired image classification algorithm for hyperspectral data using three-dimensional log-Gabor features, Opt. - Int. J. Light Electron Opt., 125 (20), 6236–6241, 2014.
26 Zhang, J., Zhao, H., Liang, J., Continuous rotation invariant local descriptors for texton dictionary-based texture classification, Comput. Vis. Image Underst., 117 (1), 56–75, 2013.
27 Kaya, A., Keçeli, A.S., Can, A.B., Examination of various classification strategies in classification of lung nodule characteristics, J. Fac. Eng. Archit. Gazi Univ., 2018, https://doi.or./10.17341/gazimmfd.416530.
28 Bergstra, J., Bastien, F., Breuleux, O., et al., Theano: Deep Learning on GPUs with Python - LISA - Publications - Aigaion 2.0’, in Proceedings of the NIPS, Big Learning Workshop, 712–721, 2011.
29 Li, W., Chen, C., Su, H., Du, Q., Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification, IEEE Trans. Geosci. Remote Sens., 53 (7), 3681–3693, 2015.