Research Article
RENKSEL VE DOKUSAL ÖZELLİKLER KULLANARAK ALEV VE ALEV OLMAYAN GÖRÜNTÜ KESİTLERİNİN SINIFLANDIRILMASI
Abstract
Orman yangınları, ekolojik dengeyi olumsuz yönde etkileyen bir
afet türüdür. Orman yangınlarının olumsuz etkilerini azaltmak için yangının
yapısının iyi bilinmesi gerekir. Bu çalışmada, orman yangın görüntülerinden
elde edilen renksel ve dokusal özellikler kullanılarak alev görüntüleri
sınıflandırılmıştır. Yapılan çalışmada alev ve alev olmayan 3100 adet görüntü
kesiti üzerinde incelemeler gerçekleştirilmiştir. Renk özelliklerinin
belirlenmesinde RGB renk uzayı kullanılmıştır. Dokusal özelliklerin belirlenmesinde
ise Laws’ın Doku Enerji Ölçümleri ve Gri Seviye Eş Oluşum Matrisinden gelen
doku özellikleri kullanılmıştır. Alev ve alev olmayan kesitlerden öznitelikler
çıkarıldıktan sonra, Destek Vektör Makineleri ve Yapay Sinir Ağları ile
sınıflandırılmıştır. Sınıflandırma başarısı Destek Vektör Makineleri ile %95, Yapay
Sinir Ağları ile %96 olarak belirlenmiştir.
Keywords
References
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- B. Toptas and D. Hanbay (2017), HSI Renk Uzayı Kullanarak Alev Tespiti, Artificial Intelligence and Data Processing Symposium (IDAP), 1–5.
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- K. Kamal, R. Qayyum, S. Mathavan, and T. Zafar (2017) ,Wood defects classification using laws texture energy measures and supervised learning approach, Advanced Engineering Informatics,vol.34:125–135.
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Year 2019,
Volume: 1 Issue: 1, 1 - 12, 31.10.2019
Abstract
References
- Y. Zhao, G. Tang, and M. Xu (2015),Hierarchical detection of wildfire flame video from pixel level to semantic level, Expert Systems with Applications, 42(8): 4097–4104,
- R. Chi, Z.-M. Lu, and Q.-G. Ji (2017), Real-time multi-feature based fire flame detection in video, IET Image Processing,11(1): 31–37.
- S. Garcia-Jimenez, A. Jurio, M. Pagola, L. De Miguel, E. Barrenechea, and H. Bustince (2017), Forest fire detection: A fuzzy system approach based on overlap indices, Applied Soft Computing Journal, vol.52: 834–842.
- M. Toptaş and D. Hanbay (2017), Smoke detection using texture and color analysis in videos, Artificial Intelligence and Data Processing Symposium (IDAP), 6–9.
- X. B. Li, Y. Hua, and N. Xia (2013), Fire detecting technology based on dynamic textures, Procedia Engineering, vol. 52: 186–195.
- V. Vipin (2012), Image Processing Based Forest Fire Detection, International Journal of Emerging Technology and Advanced Engineering, 2(2): 87–95.
- B. Toptas and D. Hanbay (2017), HSI Renk Uzayı Kullanarak Alev Tespiti, Artificial Intelligence and Data Processing Symposium (IDAP), 1–5.
- D. Y. T. Chino, L. P. S. Avalhais, J. F. Rodrigues, and A. J. M. Traina (2015), BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis, Brazilian Symposium of Computer Graphic and Image Processing, 95–102.
- S. Fazekas and D. Chetverikov (2007), Analysis and performance evaluation of optical flow features for dynamic texture recognition, Image Commun., 680–691.
- K. Kamal, R. Qayyum, S. Mathavan, and T. Zafar (2017) ,Wood defects classification using laws texture energy measures and supervised learning approach, Advanced Engineering Informatics,vol.34:125–135.
- J. Zhao, Z. Zhang, S. Han, C. Qu, Z. Yuan, and D. Zhang, 2011,SVM based forest fire detection using static and dynamic features, Computer Science and Information Systems, 8(3): 821–841.
- D. Hanbay, I. Turkoglu, and Y. Demir, 2010,Modeling switched circuits based on wavelet decomposition and neural networks,” Journal of the Franklin Institute, 347(3): 607–617.
There are 12 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Software Testing, Verification and Validation |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | October 31, 2019 |
| Published in Issue | Year 2019 Volume: 1 Issue: 1 |
