ANALISIS PENINGKATAN KUALITAS GEOMETRI DENGAN MENGGUNAKAN TITIK IKAT BUNDLE ADJUSTMENT (STUDI KASUS DATA PLEIADES WILAYAH KABUPATEN MADIUN DAN KABUPATEN MAGETAN)

Pemanfaatan data resolusi spasial sangat tinggi seperti Pleaides saat ini mengalami permintaan yang tinggi. Salah satu pemanfaatan data ini untuk mendukung kebencanaan, dimana proses pengolahan otomatisasi dan cepat sangat diperlukan dan tidak terhindarkan. Citra Pleiades telah diakusisi oleh stasiun bumi LAPAN di tahun 2018. Penelitian ini mengkaji tentang peningkatan kualitas geometri citra Pleiades dengan metode titik ikat bundle adjustment (BA) untuk proses mosaik dengan wilayah studi di wilayah Kabupaten Madiun dan Magetan. Metode ini menggunakan parameter keterkaitan geometri antar scene. Keterkaitan tersebut dihubungkan dengan membuat titik ikat. Titik-titik ini berada di area pertampalan antar scene. Citra hasil proses koreksi geometri BA akan dilakukan penilaian kualitas hasil koreksi geometrinya dengan membandikan data koordinat pengukuran lapangan. Hasil penilaian kualitas akurasi koreksi geometri menunjukkan bahwa koreksi geometri menggunakan metode BA lebih mendekati titik koordinat pengukuran lapangan dibandingkan koreksi geometri tanpa BA

Photogrammetric block adjustment without control points

A simple method for close range and aerial photogrammetry applications has been developed. The method is in the form of bundle block adjustment which utilizes only the measured distance(s) between points for generating adjusted relative three dimensional (3D) coordinate system. Software based on the proposed method has been developed and tested using simulated data. The effects of block size, number and location of measured distances, and random errors on bundle block adjustments using the proposed and the conventional methods have been studied using simulated and actual photogrammetric data. It was found that the accuracy of the bundle block adjustment using the proposed method is comparable or better than the results of conventional method. The proposed method, is suitable for photogrammetrists and non-photogrammetrists in different fields such as architectural, archaeological, forensic and aerial photogrammetry, where relative 3D coordinates system may be required. It has a significant effect on reducing the overall cost of the photogrammetric project. Merging the capabilities of the developed software and Computer Aided Design (CAD) technology, especially 3D drawing generation, widens its applications areas to include recording buildings and monuments which is necessary for architectural and archaeological applications

Parallel Bundle Adjustment of High Resolution Satellite Imagery

Bundle adjustment is the process of minimizing errors in camera and three-dimensional structure parameters. The bundle adjustment process is applicable to many areas of geospatial awareness, computer vision, robotics, and imaging, both terrestrial imaging and remote sensing. In the case of remote sensing and planetary imaging, current methods do not adequately address geographic areas consisting of both a large number of images and image observations. Other application domains focus on a single portion of the bundle adjustment process, the solution of a linear system, but ignore the computation of the coefficient matrix. In this thesis we propose a fully parallel approach to the bundle adjustment problem. This approach includes parallel computation of the required partial derivatives, which also addresses load-imbalance inherent in the problem, a parallel solution to the required linear system, and novel parallel preconditioning techniques for this system. Additionally we investigate the use of a relational database to enable fast recomputation due to image addition or removal. As other research has shown, preconditioning the linear system present in the bundle adjustment problem is critical. We present two novel, parallel preconditioners, also based on the geographic information of the input data. These preconditioners are specific to the planetary imaging application domain and address the specific matrix structure that arises in this area. We show that the parallel derivative methods achieve a high level of parallel efficiency and work well with the usage of a parallel, distributed memory, linear solver. The demonstrated preconditioners make a tangible reduction in the number of required solver iterations. Lastly, because these problems are solved many times for various applications, we present a database-backed method which stores derivative information, thereby easily allowing for projects to be re-run quickly, or modified slightly without a large recomputation cost. All of these elements result in a completely parallel bundle adjustment system capable of processing large geographic areas with millions of image observations