• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.6
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• pp.633-641
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• 2008

This study introduces the method of detecting and restoring occlusion areas by using epipolar algorithm and K-means classification algorithm for true ortho-photo generation. In the past, the techniques of detecting occlusion areas are using the reference images or information of buildings. But, in this study the occlusion areas can be automatically detected by using DTM data and exterior orientation parameters. The detected occlusion areas can be restored by using anther images or the computed values which are determined in K-means classification algorithm. In addition, this method takes advantages of applying epipolar algorithm in order to find same location in overlapping areas among images.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.237-242
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• 2003

During the past, digital orthophoto is generated for rural area or low resolution image, because the accurate extraction of DEM is difficult for urban area. But, nowadays, high resolution DEM by ALS system starts to become available for urban area, so the importance of large scale digital orthophoto generation becomes increasing. In this paper, we propose and describe effective algorithm for detecting occlusion area and not only restoring occlusion area but also processing null pixels by occlusion area for minimizing the heterogeneity of digital orthophoto. With proposed algorithm, we detected occlusion area due to height of structures such as buildings, bridges, etc, and restored occlusion area using reference image. Also, The homogeneity of generated digital orthophoto was improved by using brightness correction.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.