Rachmadani, Desy (2025) Pemodelan 3D Menggunakan Pengolahan Foto Udara Metode Korelasi Berbasis Area Dan Fitur Untuk Penerapan Pemetaan Topografi. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Perkembangan teknologi Unmanned Aerial Vehicle (UAV) telah membuka peluang besar dalam pemetaan topografi skala detail, terutama di wilayah dengan medan sulit dijangkau. Penelitian ini mengkaji efektivitas dua pendekatan pencocokan citra dalam pengolahan foto udara, yaitu korelasi silang berbasis area dan algoritma Speeded Up Robust Features (SURF) berbasis fitur. Pencocokan area dilakukan dengan mengambil sembilan sampel unik berukuran 9×9 piksel, yang menghasilkan nilai korelasi tinggi namun tetap menghadapi dua kasus gagal deteksi. Sementara itu, metode SURF menunjukkan ketahanan terhadap rotasi dan perubahan skala sedang, namun membutuhkan tumpang tindih gambar minimal 60% untuk hasil optimal. Seluruh data citra kemudian dicocokkan dan dibandingkan dengan data LiDAR sebagai referensi, serta dikonversi ke koordinat UTM melalui transformasi 3D. Hasil transformasi menunjukkan tidak adanya anomali koordinat, dengan nilai Root Mean Square Error (RMSE) horizontal sebesar 1,8308 meter dan vertikal 1,8501 meter. Evaluasi akurasi spasial menghasilkan nilai CE90 sebesar 2,778 meter dan LE90 sebesar 3,0525 meter, yang memenuhi standar Kelas 2 untuk skala 1:10.000 dan Kelas 3 untuk skala 1:10.000 sesuai PERKA BIG. Hasil ini menunjukkan bahwa integrasi UAV, korelasi area, fitur SURF, dan referensi LiDAR mampu menghasilkan model topografi yang akurat dan efisien untuk keperluan pemetaan menengah. Kata Kunci : UAV Pemetaan Topografi Pencocokan Citra Korelasi Silang SURF (Speeded Up Robust Features)
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The development of Unmanned Aerial Vehicle (UAV) technology has opened up great opportunities in detailed-scale topographic mapping, especially in areas with difficult-to-reach terrain. This study examines the effectiveness of two image matching approaches in aerial photo processing, namely area-based crosscorrelation and feature-based Speeded Up Robust Features (SURF) algorithms. Area matching was performed by taking nine unique samples measuring 9×9 pixels, which produced high correlation values but still faced two cases of failed detection. Meanwhile, the SURF method showed robustness to rotation and moderate scale changes, but required a minimum image overlap of 60% for optimal results. All image data were then matched and compared with LiDAR data as a reference, and converted to UTM coordinates through 3D transformation. The transformation results showed no coordinate anomalies, with a horizontal Root Mean Square Error(RMSE) value of 1,8308 meters and a vertical 1,8501 meters. The spatial accuracy evaluation produced a CE90 value of 2,778 meters and LE90 of 3,0525 meters, which meets the Class 2 standard for a scale of 1:10,000 and Class 3 for a scale of 1:10,000 according to PERKA BIG. These results indicate that the integration of UAV, area correlation, SURF features, and LiDAR references is capable of producing an accurate and efficient topographic model for intermediate mapping purposes.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | UAV Pemetaan Topografi Pencocokan Citra Korelasi Silang SURF (Speeded Up Robust Features), UAV Topographic Mapping Image Matching Cross-Correlation SURF ((Speeded Up Robust Features) |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) > G70.212 ArcGIS. Geographic information systems. G Geography. Anthropology. Recreation > G Geography (General) > G70.217 Geospatial data G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography > GA139 Digital Elevation Model (computer program) |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29101-(S2) Master Thesis |
| Depositing User: | Desy Dwi Rachmadani |
| Date Deposited: | 28 Jul 2025 05:31 |
| Last Modified: | 28 Jul 2025 05:31 |
| URI: | http://repository.its.ac.id/id/eprint/122004 |
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