Design and Implementation of Earth Image Classification Using Unmanned Aerial Vehicle
Research in the field of image classification has been widely applied and developed, especially in the field of satellite imagery. Image classification is the process of grouping the pixels in an image into a number of classes, so that each class can describe an entity with certain characteristics. The research aims to build software that can perform the classification of earth image results from UAV (Unmanned Aerial Vehicle) monitoring. The Image converted into YUV format then classified using Fuzzy Support Vector Machine (FSVM). This research designed elements that UAVs will be used for monitoring as follows: (1) the control station, which designed the software on a computer that is used to send or receive data, and display the data in graphical form, (2) payload, using the camera to capture images and send to the control station, (3) communication system using TCP/IP protocol, and (4) UAV, using X650 quadcopter products from xaircraft. All of data can be received if it is sent by several segmented package into smaller parts. The results of image classification, the image of the monitoring carried out on the UAV sized 256 x 256 pixels with a total number of 450 training data size. It is 16x16 pixel image data. Tests performed to classify the image into 3 classes, namely agricultural area, residential area, and water area. The highest accuracy value of 77.69% obtained by the number of training data as much as 375.
Richards John, A, Remote Sensing Digital Image Analysis: An Introduction. Verlag Berlin Heidelberg: Springer.2012.
Mahmood, S., Unmanned Aerial Vehicle Communications, Master Thesis, Blecking Institute of Technology, Sweden. 2007.
Merino, L., Caballero, F., Martinez, R.J., Maza, I., Ollero, A., Automatic Forest Fire Monitoring and Measurement using Unmanned Aerial Vehicles, VI International Conference on Forest Fire Research D. X. Viegas (Ed.). 2010.
Herwitz, S.R., Johnson, L.F., Dunagan, S.E., Higgins, R.G., et al., Imaging from an Uunmanned Aerial Vehicle: Agricultural Surveillance and Decision Support. Computers and Electronics in Agriculture 44; 49(61). Elsevier. 2004.
Coifman, B., McCord, M., Mishalani, R.G., Iswalt, M., Ji, Y., Roadway Traffic Monitoring from an Unmanned Aerial Vehicle. IEE Proc. Intell. Transp. Syst. 1; 153. 2006.
Rubert, C., Fonseca, L., Velho, L., Learning Based Super-Resolution Using YUV Model for Remote Sensing Images. Conference. Proceedings of WTDCGPI. - [s.l.] : WTDCGPI. 2005.
Abe., Shingo., Abe Shingo., Takuya Inoue., Fuzzy Support Vector Machine for Multiclass Problems. Bruges, Belgia: European Symposium on Artificial Neural Network. 2002.
Takuya Inoue., Fuzzy Support Vector Machine for Multiclass Problems, European Symposium on Artificial Neural Networks, Bruges, Belgia. 2002.
Yuyong Cui,. Zhiyuan Zeng., Remote Sensing Image Classification Based on the HSI Transformation and Fuzzy Support Vector Machine, IEEE International Conference on Future Computer and Communication: pp. 632-635. 2009.
Sutrisno., Afif Supianto., Imam Cholissodin., Implementasi Metode Watershed Dan Morfologi Untuk Segmentasi Pada Citra Satelit Area Universitas Brawijaya. GRID. 1; 1:1-8. 2012.
Gonzalez, R.C., E, Woods R., Digital Image Processing. - [s.l.]: Prentice Hall. - 2nd Edition. 2002.
Article MetricsAbstract view : 173 times
PDF - 132 times
- There are currently no refbacks.
Copyright (c) 2015 Universitas Ahmad Dahlan
TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604
View TELKOMNIKA Stats
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.