Abstract

Usual workflows for production, archiving, dissemination and use of Earth observation images (both aerial and from remote sensing satellites) pose big interoperability problems, as for example: non-alignment of pixels at the different levels of the pyramids that makes it impossible to overlay, compare and mosaic different orthoimages, without resampling them and the need to apply multiple resamplings and compression-decompression cycles. These problems cause great inefficiencies in production, dissemination through web services and processing in “Big Data” environments. Most of them can be avoided, or at least greatly reduced, with the use of a common “nested grid” for mutiresolution production, archiving, dissemination and exploitation of orthoimagery, digital elevation models and other raster data. “Nested grids” are space allocation schemas that organize image footprints, pixel sizes and pixel positions at all pyramid levels, in order to achieve coherent and consistent multiresolution coverage of a whole working area. A “nested grid” must be complemented by an appropriate “tiling schema”, ideally based on the “quad-tree” concept. In the last years a “de facto standard” grid and Tiling Schema has emerged and has been adopted by virtually all major geospatial data providers. It has also been adopted by OGC in its “WMTS Simple Profile” standard. In this paper we explain how the adequate use of this tiling schema as common nested grid for orthoimagery, DEMs and other types of raster data constitutes the most practical solution to most of the interoperability problems of these types of data.

Highlights

  • Present workflows in aerial orthophoto production, storage and dissemination normally include the following steps: 1) Produce uncompressed orthophotos, by mosaicking several orthorectified aerial images in “production units”

  • If we take the strict bounding rectangles as limits for the orthoimages, they will have “nonaligned” pixels (Figure 2), because in general the upper left corner of these orthoimages will not be multiple of pixel size

  • 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 values cause a lot of problems afterwards

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Summary

PROBLEMS OF CURRENT WORKFLOWS FOR AERIAL ORTHOPHOTOS

Present workflows in aerial orthophoto production, storage and dissemination normally include the following steps: 1) Produce uncompressed orthophotos, by mosaicking several orthorectified aerial images in “production units” (normally called “sheets” for historical reasons). 6) User connects to this Web services through a light web client or through a complete desktop GIS program This workflow generates the following problems: Problem 1: Non-aligned pixels at certain pyramid levels. If we take the strict bounding rectangles as limits for the orthoimages, they will have “nonaligned” pixels (Figure 2), because in general the upper left corner of these orthoimages will not be multiple of pixel size This makes it impossible to mosaic multiple orthophotos or even overlay them in a viewer without resampling them. The reason is that image limits can be exact multiples of the original pixel size (e.g: 1m) but not of all the pyramidal pixel sizes (2m, 4m, 8m, 16m, 32m,...) In these level and the ones it is impossible to mosaic multiple orthos, or display the “virtual mosaic” without resampling them. The pixels of the borders of the wedges in this case have intermediate values

PROBLEMS FOR SATELLITE REMOTE SENSING IMAGE PROCESSING
Requirements for an optimal workflow
Pixel borders should be aligned at all levels of the pyramid
The solution: a Nested Grid
GEOGRAPHIC PROJECTION VERSUS MERCATOR PROJECTION
Web Mercator map projection
The whole Earth in a single pixel
THE QUADTREE STRUCTURE
SuperTiles and BigTiles
TILED TIFF AS CONTAINER OF TILES
JPEG compressed TIFF
All tiles pre-generated
ADDITIONAL ISSUES
Correct area and distances computation
Secant Mercator Projection
The advantages of integer pixel sizes
Reference screen resolution of 254 ppi
APPLICATION TO DIGITAL ELEVATION MODELS
Heigh interpolation
Pyramid consistency
CONCLUSSIONS
APPLICATION TO RASTER MAPS
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