Year 2019,
, 215 - 226, 01.12.2019
Tuğcan Dündar
,
Taner İnce
References
- Bruzzone, L., Chi, M. ve Marconcini, M. (2006). A Novel Transductive SVM for Semisupervised Classification of RemoteSensing Images. IEEE Transactions on Geoscience and Remote Sensing, 44(11), 3363-3373.
- Chen, Y., Nasrabadi, N. M. ve Tran, T. D. (2011). Hyperspectral Image Classification Using Dictionary-Based Sparse Representation. IEEE Transactions on Geoscience and
Remote Sensing, 49(10), 3973-3985.
- Chi, M. ve Bruzzone, L. (2007). Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal. IEEE, 45(6), 1870-1880.
- Christophe, E., Leger, D. ve Mailhes, C. (2005). Quality criteria benchmark for hyperspectral imagery. IEEE, 43(9), 2103-2114.
- Dalponte, M., Ørka, H. O., Gobakken, T., Gianelle, D. ve Næsset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE, 51(5), 2632-2645.
- Datt, B., McVicar, T. R., Niel, T. G. V., Jupp, D. L. B. ve Pearlman, J. S. (2003). Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural
indexes. IEEE, 41(6), 1246-1259.
- Dundar, T. ve Ince, T. (2019). Sparse Representation-Based Hyperspectral Image Classification Using Multiscale Superpixels and Guided Filter. IEEE Geoscience and
Remote Sensing Letters, 16(2), 246-250.
- Fang, L., Li, S., Kang, X. ve 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.
- Fang, L., Li, S., Kang, X. ve Benediktsson, J. A. (2015). Spectral–Spatial Classification of Hyperspectral Images With a SuperpixelBased Discriminative Sparse Model. IEEE
Transactions on Geoscience and Remote Sensing, 53(8), 4186-4201.
- Garcia, M. ve Ustin, S. L. (2001). Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California. IEEE, 39(7), 1480-
1490.
- Goel, P. K., Prasher, S. O., Patel, R. M., Landry, J. A., Bonnell, R. B. ve Viau, A. A. (2003). Classification of hyperspectral data by decision trees and artificial neural networks
to identify weed stress and nitrogen status of corn. Comput. Electron. Agricult., 39(2), 67-93.
- Gualtieri, J. A. ve Cromp, R. F. (1999). Support vector machines for hyperspectral remote sensing classification. Proc. SPIE içinde (C. 3584, ss. 221-232).
- Ham, J., Chen, Y., Crawford, M. M. ve Ghosh, J. (2005). Investigation of the random forest framework for classification of hyperspectral data. IEEE, 43(3), 492-501.
- Keshava, N. (2004). Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Transactions on
Geoscience and Remote Sensing, 42(7), 1552-1565.
- Li, J., Bioucas-Dias, J. M. ve Plaza, A. (2012). Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random
Fields. IEEE, 50(3), 809-823.
- Li, J., Bioucas-Dias, J. M. ve Plaza, A. (2013). Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression. IEEE, 10(2), 318-322.
- Li, W. ve Du, Q. (2014). Joint Within-Class Collaborative Representation for Hyperspectral Image Classification. IEEE, 7(6), 2200-2208.
- Ma, L., Crawford, M. M. ve Tian, J. (2010). Local Manifold Learning-Based k-NearestNeighbor for Hyperspectral Image Classification. IEEE, 48(11), 4099-4109.
- Manolakis, D. ve Shaw, G. (2002). Detection algorithms for hyperspectral imaging applications. IEEE, 19(1), 29-43.
- Melgani, F. ve Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote
Sensing, 42(8), 1778-1790.
- Ratle, F., Camps-Valls, G. ve Weston, J. (2010). Semisupervised Neural Networks for Efficient Hyperspectral Image Classification. IEEE, 48(5), 2271-2282.
- Shukla, A. ve Kot, R. (2016). An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines. IRA-Int. J. Appl. Sci., 5(2), 85–90.
- Stavrakoudis, D. G., Galidaki, G. N., Gitas, I. Z. ve Theocharis, J. B. (2012). A Genetic FuzzyRule-Based Classifier for Land Cover Classification From Hyperspectral Imagery. IEEE, 50(1), 130-148.
- Sun, X., Qu, Q., Nasrabadi, N. M. ve Tran, T. D. (2014). Structured Priors for SparseRepresentation-Based Hyperspectral Image Classification. IEEE, 11(7), 1235-1239.
- Tropp, J. A. ve Gilbert, A. C. (2007). Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit. IEEE Transactions on Information Theory, 53(12), 4655-4666.
- Tu, B., Zhang, X., Kang, X., Zhang, G., Wang, J. ve Wu, J. (2018). Hyperspectral Image Classification via Fusing Correlation Coefficient and Joint Sparse Representation. IEEE Geoscience and Remote Sensing Letters, 15(3), 340-344.
- Yan, L., Cui, M. ve Prasad, S. (2018). Joint Euclidean and Angular Distance-Based Embeddings for Multisource Image Analysis. IEEE Geoscience and Remote Sensing Letters, 15(7), 1110-1114.
- Zhang, H., Li, J., Huang, Y. ve Zhang, L. (2014). A Nonlocal Weighted Joint Sparse Representation Classification Method for Hyperspectral Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2056-2065.
- Zhong, Y. ve Zhang, L. (2012). An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery. IEEE, 50(3), 894-909.
Spektral Eşleştirme Yöntemleri Kullanarak Hiperspektral Görüntülerin Seyrek Gösterim Tabanlı Sınıflandırılması
Year 2019,
, 215 - 226, 01.12.2019
Tuğcan Dündar
,
Taner İnce
Abstract
Seyrek gösterim tabanlı yaklaşımlar sinyal ve görüntü işleme alanlarında gösterdikleri performanstan dolayı son zamanlarda hiperspektral görüntüler üzerine de uygulanmaya başlanmış ve başarılı sonuçlar sağlanmıştır. Hiperspektal görüntü içerisindeki uzamsal bilginin de sınıflandırma işlemine dahil edilebilmesi için ortak seyrek gösterim sınıflandırıcı (OSGS) modeli geliştirilmiştir. Fakat bu modelde test pikseli etrafındaki sabit boyutlu bir pencere içerisindeki tüm komşu piksellerin ağırlık oranlarının eşit olduğu varsayılmaktadır. Özellikle de pencere boyutu arttıkça farklı sınıfa ait piksellerin sınıflandırma işlemine dahil olacağı düşünülürse hata payı artacaktır. Bu soruna bir çözüm üretebilmek için pencere içerisindeki merkez test pikseli ve her bir komşu piksele 3 adet spektral eşleştirme yöntemi uygulayıp OSGS ile birleştiren 3SE–OSGS metodu önerilmiştir. Eşleştirme yöntemlerinden elde edilen verilere ve eşik değerine göre ilgili komşu pikselin seçilmesi veya seçilmemesi sağlanmıştır.
References
- Bruzzone, L., Chi, M. ve Marconcini, M. (2006). A Novel Transductive SVM for Semisupervised Classification of RemoteSensing Images. IEEE Transactions on Geoscience and Remote Sensing, 44(11), 3363-3373.
- Chen, Y., Nasrabadi, N. M. ve Tran, T. D. (2011). Hyperspectral Image Classification Using Dictionary-Based Sparse Representation. IEEE Transactions on Geoscience and
Remote Sensing, 49(10), 3973-3985.
- Chi, M. ve Bruzzone, L. (2007). Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal. IEEE, 45(6), 1870-1880.
- Christophe, E., Leger, D. ve Mailhes, C. (2005). Quality criteria benchmark for hyperspectral imagery. IEEE, 43(9), 2103-2114.
- Dalponte, M., Ørka, H. O., Gobakken, T., Gianelle, D. ve Næsset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE, 51(5), 2632-2645.
- Datt, B., McVicar, T. R., Niel, T. G. V., Jupp, D. L. B. ve Pearlman, J. S. (2003). Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural
indexes. IEEE, 41(6), 1246-1259.
- Dundar, T. ve Ince, T. (2019). Sparse Representation-Based Hyperspectral Image Classification Using Multiscale Superpixels and Guided Filter. IEEE Geoscience and
Remote Sensing Letters, 16(2), 246-250.
- Fang, L., Li, S., Kang, X. ve 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.
- Fang, L., Li, S., Kang, X. ve Benediktsson, J. A. (2015). Spectral–Spatial Classification of Hyperspectral Images With a SuperpixelBased Discriminative Sparse Model. IEEE
Transactions on Geoscience and Remote Sensing, 53(8), 4186-4201.
- Garcia, M. ve Ustin, S. L. (2001). Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California. IEEE, 39(7), 1480-
1490.
- Goel, P. K., Prasher, S. O., Patel, R. M., Landry, J. A., Bonnell, R. B. ve Viau, A. A. (2003). Classification of hyperspectral data by decision trees and artificial neural networks
to identify weed stress and nitrogen status of corn. Comput. Electron. Agricult., 39(2), 67-93.
- Gualtieri, J. A. ve Cromp, R. F. (1999). Support vector machines for hyperspectral remote sensing classification. Proc. SPIE içinde (C. 3584, ss. 221-232).
- Ham, J., Chen, Y., Crawford, M. M. ve Ghosh, J. (2005). Investigation of the random forest framework for classification of hyperspectral data. IEEE, 43(3), 492-501.
- Keshava, N. (2004). Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Transactions on
Geoscience and Remote Sensing, 42(7), 1552-1565.
- Li, J., Bioucas-Dias, J. M. ve Plaza, A. (2012). Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random
Fields. IEEE, 50(3), 809-823.
- Li, J., Bioucas-Dias, J. M. ve Plaza, A. (2013). Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression. IEEE, 10(2), 318-322.
- Li, W. ve Du, Q. (2014). Joint Within-Class Collaborative Representation for Hyperspectral Image Classification. IEEE, 7(6), 2200-2208.
- Ma, L., Crawford, M. M. ve Tian, J. (2010). Local Manifold Learning-Based k-NearestNeighbor for Hyperspectral Image Classification. IEEE, 48(11), 4099-4109.
- Manolakis, D. ve Shaw, G. (2002). Detection algorithms for hyperspectral imaging applications. IEEE, 19(1), 29-43.
- Melgani, F. ve Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote
Sensing, 42(8), 1778-1790.
- Ratle, F., Camps-Valls, G. ve Weston, J. (2010). Semisupervised Neural Networks for Efficient Hyperspectral Image Classification. IEEE, 48(5), 2271-2282.
- Shukla, A. ve Kot, R. (2016). An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines. IRA-Int. J. Appl. Sci., 5(2), 85–90.
- Stavrakoudis, D. G., Galidaki, G. N., Gitas, I. Z. ve Theocharis, J. B. (2012). A Genetic FuzzyRule-Based Classifier for Land Cover Classification From Hyperspectral Imagery. IEEE, 50(1), 130-148.
- Sun, X., Qu, Q., Nasrabadi, N. M. ve Tran, T. D. (2014). Structured Priors for SparseRepresentation-Based Hyperspectral Image Classification. IEEE, 11(7), 1235-1239.
- Tropp, J. A. ve Gilbert, A. C. (2007). Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit. IEEE Transactions on Information Theory, 53(12), 4655-4666.
- Tu, B., Zhang, X., Kang, X., Zhang, G., Wang, J. ve Wu, J. (2018). Hyperspectral Image Classification via Fusing Correlation Coefficient and Joint Sparse Representation. IEEE Geoscience and Remote Sensing Letters, 15(3), 340-344.
- Yan, L., Cui, M. ve Prasad, S. (2018). Joint Euclidean and Angular Distance-Based Embeddings for Multisource Image Analysis. IEEE Geoscience and Remote Sensing Letters, 15(7), 1110-1114.
- Zhang, H., Li, J., Huang, Y. ve Zhang, L. (2014). A Nonlocal Weighted Joint Sparse Representation Classification Method for Hyperspectral Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2056-2065.
- Zhong, Y. ve Zhang, L. (2012). An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery. IEEE, 50(3), 894-909.