Uzaktan Algılama Görüntülerinin Evrişimsel Sinir Ağları ve Komşuluk Bileşen Analizi Tabanlı Özniteliklerinin Sınıflandırılması

Bu çalışmada, WHU-RS19 veri setinden elde edilen uzaktan algılama görüntülerinin sınıflandırması içinfarklı derin öğrenme modellerinden alınan özniteliklerin komşuluk bileşen analizi ile indirgenip DestekVektör Makinesi (DVM) ile sınıflandırması yapılmıştır. WHU-RS19 veri setinin görüntüleri ESAmodellerinden AlexNet, VGG-16 ve GoogleNet’e girdi olarak verilmiş ve her bir mimarinin son tam bağlıkatmanından 1000’er adet öznitelik elde edilmiştir. Ayrıca üç mimariden elde edilen özniteliklerbirleştirilerek komşuluk bileşen analizi (KBA) yöntemiyle 1000 özniteliğe indirgenmiştir. Aynı veriyikullanan diğer çalışmalar ile kıyaslama yapılabilmesi için mevcut verilerin %60 ve %40’ı kullanılarakeğitimi DVM ile gerçekleştirilmiştir. Çalışma kapsamında KBA ile özniteliği indirgenmiş verilerin %60’ıeğitim olarak kullanıldığında %98.75, %40’ı eğitim olarak kullanıldığında ise %97.01 oranında başarımelde edilmiştir. Bu başarım oranları mevcut çalışmalara göre daha üstün performans sağladığıgörülmüştür.

Classification of Remote Sensing Images Based on Convolutional Neural Networks and Neighborhood Component Analysis Features

In this study, for the classification of the images obtained from the WHU-RS19 dataset, the features obtained from different deep learning models were reduced by neighboring component analysis (NCA) and classified with the Support Vector Machine (SVM). The images of the WHU-RS19 data set were given as input to the CNN models AlexNet, VGG-16 and GoogleNet and 1000 features were obtained from the last fully connected layer of each architecture. Furthermore, the features obtained from the three architectures were combined and reduced to 1000 features by NCA method. In order to make comparisons with other studies that use the same data, 60% and 40% of the existing data were trained with SVM. In the study, when the 60% of the data were used as training 98.75% accuracy obtained. When the 40% of the data were used as training %97.0.1 accuracy obtained. It has been found that superior performance compared to current studies.

PDF

___

Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2018). The history began from AlexNet: a comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164.
Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep machine learning-a new frontier in artificial intelligence research. IEEE computational intelligence magazine, 5(4), 13-18.
Chaib, S., Liu, H., Gu, Y., & Yao, H. (2017). Deep feature fusion for VHR remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(8), 4775-4784.
Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015). Effective and efficient midlevel visual elementsoriented land-use classification using VHR remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 53(8), 4238-4249.
Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 119-132.
Çalışkan, A. (2018). İki farklı bölge için uzaktan algılama yöntemlerine dayalı olarak ısı adaları ve şehirleşme analizi, Yüksek Lisans Tezi, İstanbul Aydın Üniversitesi fen bilimleri enstitüsü, İstanbul, 66.
Göksu, Ö., & Aptoula, E. (2018, May). Content based image retrieval of remote sensing images based on deep features. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
Gong, Z., Zhong, P., Yu, Y., & Hu, W. (2017). Diversitypromoting deep structural metric learning for remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing, 56(1), 371-390.
Han, J., Zhang, D., Cheng, G., Liu, N., & Xu, D. (2018). Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Processing Magazine, 35(1), 84-100. Doi: 10.1109/MSP.2017.2749125
Huang, Z., Zhang, Y., Li, Q., Li, Z., Zhang, T., Sang, N., & Xiong, S. (2019). Unidirectional variation and deep CNN denoiser priors for simultaneously destriping and denoising optical remote sensing images. International Journal of Remote Sensing, 40(15), 5737-5748.
Hu, Q., Wu, W., Xia, T., Yu, Q., Yang, P., Li, Z., & Song, Q. (2013). Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping. Remote Sensing, 5(11), 6026-6042.
Hu, F., Xia, G. S., Hu, J., & Zhang, L. (2015). Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11), 14680-14707.
Khan, S., Islam, N., Jan, Z., Din, I. U., & Rodrigues, J. J. C. (2019). A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters, 125, 1-6.
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).
Lin, Y. L., & Wei, G. (2005, August). Speech emotion recognition based on HMM and SVM. In 2005 international conference on machine learning and cybernetics (Vol. 8, pp. 4898-4901). IEEE.
Lv, Q., Dou, Y., Niu, X., Xu, J., & Li, B. (2014, July). Classification of land cover based on deep belief networks using polarimetric RADARSAT-2 data. In 2014 IEEE Geoscience and Remote Sensing Symposium (pp. 4679-4682). IEEE.
Mutlu, H. E. (2018). Hiperspektral görüntü ve lidar verisinin derin öğrenme ile sınıflandırılması (Yüksek Lisans Tezi, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 67.
Özyurt, F. (2020). A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine. Soft Computing, 1-10.
Özyurt, F, Avcı, E. (2019). İmge Sınıflandırması için Yeni Öznitelik Çıkarım Yöntemi: Add-Tda Algısal Özet Fonksiyonu Tabanlı Evrişimsel Sinir Ağ (Add-TdaEsa). Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 12 (1), 30-38.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 1–9.
Qassim, H., Verma, A., & Feinzimer, D. (2018). Compressed residual-VGG16 CNN model for big data places image recognition. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 169-175). IEEE. Doi: 10.1109/CCWC.2018.8301729
Qi, K., Yang, C., Guan, Q., Wu, H., & Gong, J. (2017). A multiscale deeply described correlatons-based model for land-use scene classification. Remote Sensing, 9(9), 917.
Tuncer, T., & Ertam, F. (2019). Neighborhood component analysis and reliefF based survival recognition methods for Hepatocellular carcinoma. Physica A: Statistical Mechanics and its Applications, 123143.
Xia, G. S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., ... & Lu, X. (2017). AID: A benchmark data set for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7), 3965-3981.
Yang, J., Guo, J., Yue, H., Liu, Z., Hu, H., & Li, K. (2019). CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing.
Zhang, W., Tang, P., & Zhao, L. (2019). Remote Sensing Image Scene Classification Using CNNCapsNet. Remote Sensing, 11(5), 494.