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| Zitatschlüssel | Musyarofah.2020 |
|---|---|
| Autor | Musyarofah and Valentina Schmidt and Martin Kada |
| Seiten | 1–11 |
| Jahr | 2020 |
| DOI | 10.1088/1755-1315/500/1/012090 |
| Journal | IOP Conference Series: Earth and Environmental Science |
| Jahrgang | 500 |
| Verlag | IOP Publishing |
| Zusammenfassung | The most fundamental task in remote sensing data processing and analysis is object detection. It plays an important role in classification and very useful for various applications such as forestry, urban planning, agriculture, land use and land cover mapping, etc. However, it has many challenges to find an appropriate method due to many variations in the appearance of the object in image. The object may have occlusion, illumination, viewpoint variation, shadow, etc. Many object detection method has been researched and developed. Recently, the development of various machine learning-based methods for object detection has been increasing. Among of them are methods based on artificial neural network, deep learning and its derivatives. In this research, object detection method of aerial image by using mask-region convolutional neural network (mask-R CNN) is developed. The result shows that this method gives a significant accuracy by increasing the image training and epoch time. |
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