3D Modeling Using Aerial Photo Processing, Area and Feature Based Correlation Method and Lidar Data as Comparative Data for the Application of Topographic Mapping

Image matching (image matching) is one of the basic components in various digital applications, especially in the fields of topographic mapping and geographic information systems (GIS). In general, image matching approaches can be classified into two main methods, namely area-based and feature-based. Area-based methods compare spatial similarities between image pixels directly, while feature-based methods focus on detecting and matching similar points or structures between images. In this study, the area-based method is implemented using the cross-correlation technique.cross correlationto measure the similarity of pixel intensity between image regions, while feature-based methods use algorithmsSpeeded Up Robust Features (SURF)to detect and match keypoints that are invariant to changes in scale, rotation, and other geometric transformations. Evaluations were performed on topographic image datasets with varying lighting conditions and orientations, showing that the SURF method yields higher accuracy in the case of complex transformations, while cross-correlation is more computationally efficient for images with minimal transformations. Testing consisted of taking several points from the results of photo processing using the algorithmSURFfor X, Y, Z coordinates, where there are 9 sample points tested. From the results of the 9 sample points tested, there are 2 points that have very high difference values when compared to LiDAR data processing. This error is likely caused by the inconsistency of feature detection in low-Texture images or the presence of occlusion in the test area, which results in inaccuracy in the estimation of three-dimensional coordinates.