Image Mapping Detection of Green Areas Using Speed Up Robust Features

Afriansyah, Faisal Lutfi and Muna, Niyalatul and Widiastuti, Ika and Fanani, Nurul Zainal and Purnomo, Fendik Eko (2019) Image Mapping Detection of Green Areas Using Speed Up Robust Features. In: 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), 16-17 Oct. 2019, Jember.

[img] Text (Full Text)
2. Image Mapping Detection of Green Areas Using.pdf - Published Version

Download (525kB)
[img] Text (Hasil Cek Similarity)
Image Mapping Detection of Green Areas Using Speed Up Robust Features.pdf - Supplemental Material

Download (2MB)
[img] Text (Hasil Peer Review)
15. Image Mapping Detection...pdf - Supplemental Material
Restricted to Repository staff only

Download (604kB)
Official URL:


Development of mapping and remote sensing to detection of green areas in a wide range can do aerial photography using drones. The aerial photo in question is a small format aerial photo using a camera. The image produced from aerial photographs is still fragmented into separate parts. Therefore, it is necessary to merge each sequential image. Merging is done by detecting the mapping of the area by sewing each image based on the point of similarity in pixels. The method applied with the search for similar features uses the Speeded Up Robust Features (SURF). The results obtained to see the level of similarity in the feature mapping area so that the merger into one detected area does not require a long time. The SURF method is applied, giving the results of the number of images that correspond to the Minimum Mean Square Error (MSE) level of 0.0246. The results obtained are the level of similarity at matched point 32 gives a panoramic view approaching the mapping according to the green area of the aerial photo.

Item Type: Conference or Workshop Item (Paper)
Subjects: 410 - Rumpun Ilmu Teknik > 450 - Teknik Elektro dan Informatika > 457 - Teknik Komputer
Depositing User: Nurul Zainal Fanani
Date Deposited: 21 Oct 2021 05:09
Last Modified: 17 Jan 2023 22:54

Actions (login required)

View Item View Item