Araştırma Makalesi
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Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması

Yıl 2022, Cilt: 22 Sayı: 6, 1368 - 1379, 28.12.2022
https://doi.org/10.35414/akufemubid.1177912

Öz

Hiperspektral Görüntüler (HSG), sağladığı yüksek spektral çözünürlük sayesinde birçok alanda kullanım alanına sahiptir. HSG’lerin sınıflandırılması, görüntülerin yüksek spektral çözünürlüğü sebebiyle zorlayıcı bir süreçtir. Bu bağlamda HSG’lerin sınıflandırılmasında birçok Makine Öğrenme (MÖ) algoritmasının performansı araştırılmıştır. Özellikle Derin Öğrenmenin alt dallarından biri olan Evrişimli Sinir Ağları (ESA) tabanlı birçok ağ mimarisi HSG’lerin sınıflandırılması için özel olarak geliştirilmiştir. Hiperspektral görüntüleme sistemlerinin (HGS) yüksek maliyetleri sebebiyle veri setlerinin elde edilmesi zordur. Son yıllarda insanlı ve insansız hava araçları (İHA) için geliştirilen yeni nesil hiperspektral görüntüleme sistemlerinin maliyetleri giderek düşmekte olup yüksek mekânsal çözünürlüklü ve uygun maliyetli HSG elde edilmesi mümkün hale gelmiştir. Bu çalışmada çeşitli platformlardan elde edilmiş farklı spektral ve uzamsal çözünürlükteki HSG’lerin sınıflandırılmasında çeşitli MÖ algoritmalarının performansının incelenmesi amaçlanmıştır. Bu kapsamda uydu tabanlı HyRANK Loukia, hava aracı tabanlı Chikusei İHA tabanlı WHU-Hi HanChuan isimli görüntüler Destek Vektör Makineleri, Rastgele Orman ve ESA algoritmaları kullanılarak sınıflandırılmıştır. Sınıflandırma performansları incelendiğinde en yüksek genel doğruluk değerleri veri setleri için sırasıyla %87,78, %99,82 ve %96,89 olarak ESA tarafından elde edildiği görülmüştür.

Destekleyen Kurum

Afyon Kocatepe Üniversitesi Bilimsel Araştırma Projesi Koordinatörlüğü

Proje Numarası

20.FEN.BİL.12

Teşekkür

Yazarlar HyRANK veri setini sağlayan ISPRS Komisyon III WG III/4'e; Chikusei Hyperspectral Data veri setini sağlayan Tokyo Üniversitesine; WHU-Hi veri setini sağlayan RSIDEA akademik araştırma grubuna, LIESMARS’a ve Wuhan Üniversitesine teşekkür ederler. Bu çalışma AKÜ BAPK tarafından 20.FEN.BİL.12 numaralı proje kapsamında desteklenmiştir.

Kaynakça

  • Adao T., Hruska J., Padua L., Bessa J., Peres E., Morais R. et al., 2017. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, 9(11), 1110.
  • Akar O. and Tunc Gormus E., 2021. Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information. Geocarto International, 37(14), 3963-3990.
  • Ardouin J.P., Levesque J. and Rea T.A., 2007. A demonstration of hyperspectral image exploitation for military applications, 2007 10th International Conference on Information Fusion, 1-8.
  • Audebert N., Le Saux B. and Lefevre S., 2019. Deep learning for classification of hyperspectral data: A comparative review. IEEE Geoscience and Remote Sensing Magazine, 7(2), 159-173.
  • Bhosle K. and Musande V., 2020, Evaluation of CNN model by comparing with convolutional autoencoder and deep neural network for crop classification on hyperspectral imagery. Geocarto International, 1(15), 813-827.
  • Boser B.E., Guyon I.M. and Vapnik V.N., 1992. A training algorithm for optimal margin classifiers. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, 144-152.
  • Breiman L., 2001. Random Forests. Machine learning, 45(1), 5-32.
  • Chan J.C.W. and Paelinckx D., 2008. Evaluation of Random Forest and Adaboost Tree-based Ensemble Classification and Spectral Band Selection for Ecotope Mapping Using Airborne Hyperspectral Imagery. Remote Sensing of Environment, 112(6), 2999-3011.
  • Chen S., Jin M. and Ding J., 2020. Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network. Multimedia Tools and Applications, 80(2), 1859-1882.
  • Christovam L.E., Pessoa G.G., Shimabukuro M.H. and Galo M.L.B.T., 2019. Land use and land cover classification using hyperspectral imagery: evaluating the performance of spectral angle mapper, support vector machine and random forest. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 10–14 June 2019, Enschede, The Netherlands, 1841-1847.
  • Congalton R.G. and Green K., 2019. Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, 328, Boca Raton, FL.
  • Crucil G., Castaldi F., Aldana-Jague E., van Wesemael B., Macdonald A. and Van Oost K., 2019. Assessing the performance of UAS-compatible multispectral and hyperspectral sensors for soil organic carbon prediction. Sustainability, 11(7), 1889.
  • Erturk A., Iordache M.D. and Plaza A., 2015. Sparse unmixing-based change detection for multitemporal hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(2), 708-719.
  • Foody G.M., 2004. Thematic map comparison. Photogrammetric Engineering & Remote Sensing, 70(5), 627-633.
  • Ghanbari H., Mahdianpari M., Homayouni S. and Mohammadimanesh F., 2021. A meta-analysis of convolutional neural networks for remote sensing applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3602-3613.
  • Ghatak A., 2019. Deep Learning with R, Springer, 245, Kolkata.
  • Gualtieri J.A. and Cromp R.F., 1999. Support vector machines for hyperspectral remote sensing classification. 27th AIPR Workshop: Advances in Computer-Assisted Recognition, Washington DC, 221-232.
  • Guo Y., Liu Y., Oerlemans A., Lao S., Wu S. and Lew M.S., 2016. Deep Learning for Visual Understanding: A Review. Neurocomputing, 187, 27-48.
  • Hang R., Li Z., Liu Q., Ghamisi P. and Bhattacharyya S.S., 2020. Hyperspectral Image Classification with Attention Aided CNNs. arXiv preprint arXiv:2005.11977.
  • Heiden U., Heldens W., Roessner S., Segl K., Esch T. and Mueller A., 2012. Urban structure type characterization using hyperspectral remote sensing and height information. Landscape and urban Planning, 105(4), 361-375.
  • Hsu C.W., Chang C.C. and Lin C.J., 2003. A practical guide to support vector classification. Taipei, Taiwan. Jordan M.I. and Mitchell T.M., 2015. Machine learning: trends, perspectives, and prospects. Science, 349(6245), 255-260.
  • Karantzalos K., Karakizi C., Kandylakis Z. and Antoniou G., 2018. HyRANK Hyperspectral Satellite Dataset I (Version v001).
  • Kavzoglu T. and Colkesen I., 2009. A kernel functions analysis for support vector machines for land cover classification, International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359.
  • Kavzoglu T., Tonbul H., Yildiz Erdemir M. and Colkesen I., 2018. Dimensionality reduction and classification of hyperspectral images using object-based image analysis, Journal of the Indian Society of Remote Sensing, 46(8), 1297-1306.
  • Krizhevsky A., Sutskever I. and Hinton G.E., 2012. ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, Lake Tahoe, Nevada, 1097–1105.
  • LeCun Y., Bengio Y. and Hinton G., 2015. Deep learning. Nature, 521(7553), 436-444.
  • LeCun Y., Bottou L., Bengio Y. and Haffner P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
  • Li Y., Zhang H., Xue X., Jiang Y. and Shen Q., 2018. Deep learning for remote sensing image classification: a survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(6), e1264.
  • Loggenberg K., Strever A., Greyling B. and Poona N., 2018. Modelling water stress in a Shiraz vineyard using hyperspectral imaging and machine learning. Remote Sensing, 10(2), 202.
  • Lu G. and Fei B., 2014. Medical hyperspectral imaging: a review. Journal of Biomedical Optics, 19(1), 010901. Luo Y., Zou J., Yao C., Zhao X., Li T. and Bai G., 2018. HSI-CNN: a novel convolution neural network for hyperspectral image. 2018 International Conference on Audio, Language and Image Processing (ICALIP), Beijing, 464-469.
  • Meng Z., Zhao F., Liang M. and Xie W., 2021. Deep residual involution network for hyperspectral image classification. Remote Sensing, 13(16), 3055.
  • Mountrakis G., Im J. and Ogole C., 2011. Support vector machines in remote sensing: a review, ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247-259.
  • Pal M., 2005. Random forest classifier for remote sensing classification. International Journal of remote sensing, 26(1), 217-222.
  • Pal M. and Mather P., 2005. Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007-1011.
  • Rodriguez-Galiano V.F., Ghimire B., Rogan J., Chica-Olmo M. and Rigol-Sanchez J.P., 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93-104.
  • Roy S.K., Krishna G., Dubey S.R. and Chaudhuri B.B., 2019. HybridSN: exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 17(2), 277-281.
  • Sahin E.K., Colkesen I. and Kavzoglu T., 2020. A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto International, 35(4), 341-363.
  • Salami E., Barrado C. and Pastor E., 2014. UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sensing, 6(11), 11051-11081.
  • Sheykhmousa M., Mahdianpari M., Ghanbari H., Mohammadimanesh F., Ghamisi P. and Homayouni S., 2020. Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308-6325.
  • Si Y., Gong D., Guo Y., Zhu X., Huang Q., Evans J. et al., 2021. An advanced spectral–spatial classification framework for hyperspectral imagery based on DeepLab v3+. Applied Sciences, 11(12), 5703.
  • Sunar F., Özkan C. ve Osmanoğlu B., 2011. Uzaktan Algılama. Anadolu Üniversitesi Yayınları, 210, Eskişehir. Teke M., Deveci H.S., Haliloğlu O., Gürbüz S.Z. ve Sakarya U., 2013. A short survey of hyperspectral remote sensing applications in agriculture. 2013 6th International Conference on Recent Advances in Space Technologies (RAST), 171-176.
  • Van der Meer F.D., Van der Werff H.M., Van Ruitenbeek F.J., Hecker C.A., Bakker W.H., Noomen M.F. et al., 2012. Multi-and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation, 14(1), 112-128.
  • Vapnik V., 1995. The nature of statistical learning theory, Springer - Verlag, 188, New York. Wang Y., Li Y., Song Y. and Rong X., 2020. The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences, 10(5), 1897.
  • Waske B., Benediktsson J.A., Arnason K., Sveinsson J.R., 2009. Mapping of hyperspectral AVIRIS data using machine-learning algorithms. Canadian Journal of Remote Sensing, 35(sup1), 106-116.
  • Xia J., Yokoya N. and Iwasaki A., 2016. Hyperspectral image classification with canonical correlation forests. IEEE Transactions on Geoscience and Remote Sensing, 55(1), 421-431.
  • Yokoya N. and Iwasaki A., 2016. Airborne hyperspectral data over Chikusei. Space Appl. Lab., Univ. Tokyo, Tokyo, Japan, Tech. Rep. SAL-2016-05-27, 5.
  • Zhong Y., Hu X., Luo C., Wang X., Zhao J. and Zhang L., 2020. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF, Remote Sensing of Environment, 250, 112012.

Comparison of Performances of CNN, SVM and RF Algorithms in Classification of Hyperspectral Images Obtained from Different Platforms

Yıl 2022, Cilt: 22 Sayı: 6, 1368 - 1379, 28.12.2022
https://doi.org/10.35414/akufemubid.1177912

Öz

Hyperspectral Images (HSI) are employed in many fields, owing to the high spectral resolution that offer. Classification of HSIs is a challenging process due to the high spectral resolution of the images. In this regard, the performance of various Machine Learning (ML) algorithms in the classification of HSGs have been investigated. Especially, Convolutional Neural Networks (CNN) architectures, have been specially developed for the classification of HSIs. Due to the high cost of hyperspectral imaging instruments, obtaining HSI datasets is challenging. In recent years, the costs of new generation hyperspectral imaging systems developed for manned and unmanned aerial vehicles (UAV) have been decreasing, and it has become possible to obtain high spatial resolution and cost-effective HSIs. In this study, it is aimed to examine the performance of various ML algorithms in the classification of HSIs with different spectral and spatial resolutions obtained from various platforms. In this context, satellite-based HyRANK Loukia, manned aircraft-based Chikusei and UAV-based WHU-Hi HanChuan images were classified using Support Vector Machines, Random Forest and CNN algorithms. When the classification performances were examined, it was seen that the highest overall accuracy values were obtained by CNN as 87.78%, 99.82% and 96.89% for the data sets, respectively.

Proje Numarası

20.FEN.BİL.12

Kaynakça

  • Adao T., Hruska J., Padua L., Bessa J., Peres E., Morais R. et al., 2017. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, 9(11), 1110.
  • Akar O. and Tunc Gormus E., 2021. Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information. Geocarto International, 37(14), 3963-3990.
  • Ardouin J.P., Levesque J. and Rea T.A., 2007. A demonstration of hyperspectral image exploitation for military applications, 2007 10th International Conference on Information Fusion, 1-8.
  • Audebert N., Le Saux B. and Lefevre S., 2019. Deep learning for classification of hyperspectral data: A comparative review. IEEE Geoscience and Remote Sensing Magazine, 7(2), 159-173.
  • Bhosle K. and Musande V., 2020, Evaluation of CNN model by comparing with convolutional autoencoder and deep neural network for crop classification on hyperspectral imagery. Geocarto International, 1(15), 813-827.
  • Boser B.E., Guyon I.M. and Vapnik V.N., 1992. A training algorithm for optimal margin classifiers. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, 144-152.
  • Breiman L., 2001. Random Forests. Machine learning, 45(1), 5-32.
  • Chan J.C.W. and Paelinckx D., 2008. Evaluation of Random Forest and Adaboost Tree-based Ensemble Classification and Spectral Band Selection for Ecotope Mapping Using Airborne Hyperspectral Imagery. Remote Sensing of Environment, 112(6), 2999-3011.
  • Chen S., Jin M. and Ding J., 2020. Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network. Multimedia Tools and Applications, 80(2), 1859-1882.
  • Christovam L.E., Pessoa G.G., Shimabukuro M.H. and Galo M.L.B.T., 2019. Land use and land cover classification using hyperspectral imagery: evaluating the performance of spectral angle mapper, support vector machine and random forest. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 10–14 June 2019, Enschede, The Netherlands, 1841-1847.
  • Congalton R.G. and Green K., 2019. Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, 328, Boca Raton, FL.
  • Crucil G., Castaldi F., Aldana-Jague E., van Wesemael B., Macdonald A. and Van Oost K., 2019. Assessing the performance of UAS-compatible multispectral and hyperspectral sensors for soil organic carbon prediction. Sustainability, 11(7), 1889.
  • Erturk A., Iordache M.D. and Plaza A., 2015. Sparse unmixing-based change detection for multitemporal hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(2), 708-719.
  • Foody G.M., 2004. Thematic map comparison. Photogrammetric Engineering & Remote Sensing, 70(5), 627-633.
  • Ghanbari H., Mahdianpari M., Homayouni S. and Mohammadimanesh F., 2021. A meta-analysis of convolutional neural networks for remote sensing applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3602-3613.
  • Ghatak A., 2019. Deep Learning with R, Springer, 245, Kolkata.
  • Gualtieri J.A. and Cromp R.F., 1999. Support vector machines for hyperspectral remote sensing classification. 27th AIPR Workshop: Advances in Computer-Assisted Recognition, Washington DC, 221-232.
  • Guo Y., Liu Y., Oerlemans A., Lao S., Wu S. and Lew M.S., 2016. Deep Learning for Visual Understanding: A Review. Neurocomputing, 187, 27-48.
  • Hang R., Li Z., Liu Q., Ghamisi P. and Bhattacharyya S.S., 2020. Hyperspectral Image Classification with Attention Aided CNNs. arXiv preprint arXiv:2005.11977.
  • Heiden U., Heldens W., Roessner S., Segl K., Esch T. and Mueller A., 2012. Urban structure type characterization using hyperspectral remote sensing and height information. Landscape and urban Planning, 105(4), 361-375.
  • Hsu C.W., Chang C.C. and Lin C.J., 2003. A practical guide to support vector classification. Taipei, Taiwan. Jordan M.I. and Mitchell T.M., 2015. Machine learning: trends, perspectives, and prospects. Science, 349(6245), 255-260.
  • Karantzalos K., Karakizi C., Kandylakis Z. and Antoniou G., 2018. HyRANK Hyperspectral Satellite Dataset I (Version v001).
  • Kavzoglu T. and Colkesen I., 2009. A kernel functions analysis for support vector machines for land cover classification, International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359.
  • Kavzoglu T., Tonbul H., Yildiz Erdemir M. and Colkesen I., 2018. Dimensionality reduction and classification of hyperspectral images using object-based image analysis, Journal of the Indian Society of Remote Sensing, 46(8), 1297-1306.
  • Krizhevsky A., Sutskever I. and Hinton G.E., 2012. ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, Lake Tahoe, Nevada, 1097–1105.
  • LeCun Y., Bengio Y. and Hinton G., 2015. Deep learning. Nature, 521(7553), 436-444.
  • LeCun Y., Bottou L., Bengio Y. and Haffner P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
  • Li Y., Zhang H., Xue X., Jiang Y. and Shen Q., 2018. Deep learning for remote sensing image classification: a survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(6), e1264.
  • Loggenberg K., Strever A., Greyling B. and Poona N., 2018. Modelling water stress in a Shiraz vineyard using hyperspectral imaging and machine learning. Remote Sensing, 10(2), 202.
  • Lu G. and Fei B., 2014. Medical hyperspectral imaging: a review. Journal of Biomedical Optics, 19(1), 010901. Luo Y., Zou J., Yao C., Zhao X., Li T. and Bai G., 2018. HSI-CNN: a novel convolution neural network for hyperspectral image. 2018 International Conference on Audio, Language and Image Processing (ICALIP), Beijing, 464-469.
  • Meng Z., Zhao F., Liang M. and Xie W., 2021. Deep residual involution network for hyperspectral image classification. Remote Sensing, 13(16), 3055.
  • Mountrakis G., Im J. and Ogole C., 2011. Support vector machines in remote sensing: a review, ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247-259.
  • Pal M., 2005. Random forest classifier for remote sensing classification. International Journal of remote sensing, 26(1), 217-222.
  • Pal M. and Mather P., 2005. Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007-1011.
  • Rodriguez-Galiano V.F., Ghimire B., Rogan J., Chica-Olmo M. and Rigol-Sanchez J.P., 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93-104.
  • Roy S.K., Krishna G., Dubey S.R. and Chaudhuri B.B., 2019. HybridSN: exploring 3-d–2-d cnn feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 17(2), 277-281.
  • Sahin E.K., Colkesen I. and Kavzoglu T., 2020. A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto International, 35(4), 341-363.
  • Salami E., Barrado C. and Pastor E., 2014. UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sensing, 6(11), 11051-11081.
  • Sheykhmousa M., Mahdianpari M., Ghanbari H., Mohammadimanesh F., Ghamisi P. and Homayouni S., 2020. Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308-6325.
  • Si Y., Gong D., Guo Y., Zhu X., Huang Q., Evans J. et al., 2021. An advanced spectral–spatial classification framework for hyperspectral imagery based on DeepLab v3+. Applied Sciences, 11(12), 5703.
  • Sunar F., Özkan C. ve Osmanoğlu B., 2011. Uzaktan Algılama. Anadolu Üniversitesi Yayınları, 210, Eskişehir. Teke M., Deveci H.S., Haliloğlu O., Gürbüz S.Z. ve Sakarya U., 2013. A short survey of hyperspectral remote sensing applications in agriculture. 2013 6th International Conference on Recent Advances in Space Technologies (RAST), 171-176.
  • Van der Meer F.D., Van der Werff H.M., Van Ruitenbeek F.J., Hecker C.A., Bakker W.H., Noomen M.F. et al., 2012. Multi-and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation, 14(1), 112-128.
  • Vapnik V., 1995. The nature of statistical learning theory, Springer - Verlag, 188, New York. Wang Y., Li Y., Song Y. and Rong X., 2020. The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences, 10(5), 1897.
  • Waske B., Benediktsson J.A., Arnason K., Sveinsson J.R., 2009. Mapping of hyperspectral AVIRIS data using machine-learning algorithms. Canadian Journal of Remote Sensing, 35(sup1), 106-116.
  • Xia J., Yokoya N. and Iwasaki A., 2016. Hyperspectral image classification with canonical correlation forests. IEEE Transactions on Geoscience and Remote Sensing, 55(1), 421-431.
  • Yokoya N. and Iwasaki A., 2016. Airborne hyperspectral data over Chikusei. Space Appl. Lab., Univ. Tokyo, Tokyo, Japan, Tech. Rep. SAL-2016-05-27, 5.
  • Zhong Y., Hu X., Luo C., Wang X., Zhao J. and Zhang L., 2020. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF, Remote Sensing of Environment, 250, 112012.
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Makaleler
Yazarlar

Eren Can Seyrek 0000-0003-1300-4898

Murat Uysal 0000-0001-5202-4387

Proje Numarası 20.FEN.BİL.12
Erken Görünüm Tarihi 15 Aralık 2022
Yayımlanma Tarihi 28 Aralık 2022
Gönderilme Tarihi 20 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 22 Sayı: 6

Kaynak Göster

APA Seyrek, E. C., & Uysal, M. (2022). Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 22(6), 1368-1379. https://doi.org/10.35414/akufemubid.1177912
AMA Seyrek EC, Uysal M. Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. Aralık 2022;22(6):1368-1379. doi:10.35414/akufemubid.1177912
Chicago Seyrek, Eren Can, ve Murat Uysal. “Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri Ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 22, sy. 6 (Aralık 2022): 1368-79. https://doi.org/10.35414/akufemubid.1177912.
EndNote Seyrek EC, Uysal M (01 Aralık 2022) Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 22 6 1368–1379.
IEEE E. C. Seyrek ve M. Uysal, “Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 22, sy. 6, ss. 1368–1379, 2022, doi: 10.35414/akufemubid.1177912.
ISNAD Seyrek, Eren Can - Uysal, Murat. “Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri Ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 22/6 (Aralık 2022), 1368-1379. https://doi.org/10.35414/akufemubid.1177912.
JAMA Seyrek EC, Uysal M. Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2022;22:1368–1379.
MLA Seyrek, Eren Can ve Murat Uysal. “Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri Ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 22, sy. 6, 2022, ss. 1368-79, doi:10.35414/akufemubid.1177912.
Vancouver Seyrek EC, Uysal M. Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2022;22(6):1368-79.