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Design and Implementation of Earth Image Classification Using Unmanned Aerial Vehicle

Journal from gdlhub / 2016-11-17 04:50:29
Oleh : Barlian Henryranu Prasetio, Ahmad Afif Supianto, Gembong Edhi Setiawan, Budi Darma Setiawan, Imam Cholissodin, Sabriansyah Rizkiqa Akbar, Telkomnika
Dibuat : 2013-09-01, dengan 1 file

Keyword : mage Transformation; Embedded System; Unmanned Aerial Vehicle; Payload
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1786

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.

Deskripsi Alternatif :

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.

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