Abstract

The paper gives an overview on the problems occurring during commercial digital orthophoto (DOP) production. There is a wide variety in requirements on orthophotos. It is no problem to produce good orthophotos in small areas, whereas production of good and homogeneous orthophotos in large areas remains a challenge. Therefore, we discuss large area orthophotos based on regular areal flight with regular flight parameters and nadir images, not on large numbers of images creating orthophotos based on various oblique images. <br><br> The focus of the paper is to optimize the process of line production of orthophotos to achieve the best results under minimum usage of resources. Beside improvements in commercial software, the improvement in process design can help to achieve this objectives. It is necessary the attach importance to the different quality aspects to invest the limited resources at the most profitable process step. <br><br> The requirements on orthophotos can be grouped into three categories: geometric, radiometric and aesthetic requirements. The main intention is to generate correct and well interpretable orthoimages, putting attention to geometry and radiometry. Many requirements cause enormous expenditures in flight time, number of images and amount of interactive editing work. <br><br> Among the geometric aspects, the accuracy is the prominent factor. The geometric quality of the underlying digital terrain model (DTM) is of outstanding importance. Depending on the used focal length during acquisition and overlap, geometric errors in the DTM are directly represented in the orthophoto. Particularly at manmade surface break lines, even horizontal DTM errors occur striking in the orthophotos. <br><br> A special consideration is to put on objects outside the DTM surface. While buildings may be represented in a good manner in a digital surface models (DSM) to generate so called “true orthophotos”, there is no way to represent vegetation, cables, poles etc. in an appropriate manner in any DSM. <br><br> Seam lines have to disappear in the mosaics. There should be no indication for the detection of the used seam lines nether in geometric nor in radiometric edges. <br><br> The radiometric aspect include a good distribution of the color values in the histogram, good contrasts should become quantified. It is impossible to measure the aesthetic quality, but is has to be homogeneous over the whole project area without eye-catching artefacts. <br><br> In the conclusion, we want to give to the producers of orthophotos and their clients a list of criteria and quality figures to be agreed on before the production starts to avoid later discussions.

Highlights

  • The basic idea of Digital Orthophotos (DOP) is the vertical projection of images of 3D objects on a defined surface, typically a map projection

  • The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B2, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic result from a separate source or at least a separate process, which is not covered in this article (Wiedemann & Peter 2006)

  • The broken circle is irritating, even when den DOP is done according to the state of the art, but based on a digital terrain model (DTM)

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Summary

QUALITY OF ORTHOIMAGES

The basic idea of Digital Orthophotos (DOP) is the vertical (orthogonal) projection of images of 3D objects on a defined surface, typically a map projection. This is the most common method of geo visualization. Sufficient contrast, natural colors and a representative appearance of the site help to interpret the situation. Even if the orthophoto is correct here, our eye is attracted by the break of the real circular pattern resulting from the seam line between the buildings in the south. The broken circle is irritating, even when den DOP is done according to the state of the art, but based on a digital terrain model (DTM)

Quality Aspects of Digital Orthophotos
Quality measurement
Flight Parameters
Geometric Aspects
Radiometric Aspects
True DOPs
Dense Matching
Findings
CONCLUSIONS
Full Text
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