Classification of Remote Sensing Images Based on Convolutional Neural Networks and Neighborhood Component Analysis Features
Abstract
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.
References
- 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.
- Doğantekin, A., Özyurt, F., Avcı, E., & Koç, M. (2019). A novel approach for liver image classification: PH-C-ELM. Measurement, 137,332-338. Doi:https://doi.org/10.1016/j.measurement.2019.01.060
- 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
- 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. Doi: https://doi.org/10.1016/j.patrec.2019.03.022
- Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017). Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3226-3229). IEEE. Doi: 10.1109/IGARSS.2017.8127684
- Özyurt, F, Avcı, E. (2019). İmge Sınıflandırması için Yeni Öznitelik Çıkarım Yöntemi: Add-Tda Algısal Özet Fonksiyonu Tabanlı Evrişimsel Sinir Ağ (Add-Tda-Esa). Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 12 (1), 30-38. Retrieved from http://dergipark.org.tr/tbbmd/issue/44695/464206
- Özyurt, F., Tuncer, T., Avci, E., Koç, M., & Serhatlioğlu, İ. (2018). A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network. Arabian Journal for Science and Engineering, 1-10. Doi: https://doi.org/10.1007/s13369-018-3454-1
- Toğaçar, M., & Ergen, B. (2018). Deep Learning Approach for Classification of Breast Cancer. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-5). IEEE. Doi: 10.1109/IDAP.2018.8620802
- Toğaçar, M., Ergen, B., & Özyurt, F. (2018, September). Deep Learning Activities On Remote Sensed Hyperspectral Images. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-4). IEEE. Doi: 10.1109/IDAP.2018.8620750.
- 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
Uzaktan Algılama Görüntülerinin Evrişimsel Sinir Ağları ve Komşuluk Bileşen Analizi Tabanlı Özniteliklerinin Sınıflandırılması
Abstract
Bu çalışmada, WHU-RS19
veri setinden elde edilen uzaktan algılama görüntülerinin sınıflandırması için
farklı derin öğrenme modellerinden alınan özniteliklerin komşuluk bileşen
analizi ile indirgenip Destek Vektör Makinesi (DVM) ile sınıflandırması
yapılmıştır. WHU-RS19 veri setinin görüntüleri ESA modellerinden AlexNet,
VGG-16 ve GoogleNet’e girdi olarak verilmiş ve her bir mimarinin son tam bağlı
katmanından 1000’er adet öznitelik elde edilmiştir. Ayrıca üç mimariden elde
edilen öznitelikler birleştirilerek komşuluk bileşen analizi (KBA) yöntemiyle
1000 özniteliğe indirgenmiştir. Aynı veriyi kullanan diğer çalışmalar ile
kıyaslama yapılabilmesi için mevcut verilerin %60 ve %40’ı kullanılarak eğitimi
DVM ile gerçekleştirilmiştir. Çalışma kapsamında KBA ile özniteliği indirgenmiş
verilerin %60’ı eğitim olarak kullanıldığında %98.75, %40’ı eğitim olarak
kullanıldığında ise %97.01 oranında başarım elde edilmiştir. Bu başarım
oranları mevcut çalışmalara göre daha üstün performans sağladığı görülmüştür.
References
- 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.
- Doğantekin, A., Özyurt, F., Avcı, E., & Koç, M. (2019). A novel approach for liver image classification: PH-C-ELM. Measurement, 137,332-338. Doi:https://doi.org/10.1016/j.measurement.2019.01.060
- 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
- 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. Doi: https://doi.org/10.1016/j.patrec.2019.03.022
- Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017). Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3226-3229). IEEE. Doi: 10.1109/IGARSS.2017.8127684
- Özyurt, F, Avcı, E. (2019). İmge Sınıflandırması için Yeni Öznitelik Çıkarım Yöntemi: Add-Tda Algısal Özet Fonksiyonu Tabanlı Evrişimsel Sinir Ağ (Add-Tda-Esa). Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 12 (1), 30-38. Retrieved from http://dergipark.org.tr/tbbmd/issue/44695/464206
- Özyurt, F., Tuncer, T., Avci, E., Koç, M., & Serhatlioğlu, İ. (2018). A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network. Arabian Journal for Science and Engineering, 1-10. Doi: https://doi.org/10.1007/s13369-018-3454-1
- Toğaçar, M., & Ergen, B. (2018). Deep Learning Approach for Classification of Breast Cancer. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-5). IEEE. Doi: 10.1109/IDAP.2018.8620802
- Toğaçar, M., Ergen, B., & Özyurt, F. (2018, September). Deep Learning Activities On Remote Sensed Hyperspectral Images. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1-4). IEEE. Doi: 10.1109/IDAP.2018.8620750.
- 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
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | December 31, 2019 |
| Submission Date | April 26, 2019 |
| Published in Issue | Year 2019 Volume: 19 Issue: 3 |
