Automatic Near-Real-Time Image Processing Chain for Very High Resolution Optical Satellite Data
In response to the increasing need for automatic and fast satellite image processing SPACE-SI has developed and implemented a fully automatic image processing chain STORM that performs all processing steps from sensor-corrected optical images (level 1) to web-delivered map-ready images and products...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/669/2015/isprsarchives-XL-7-W3-669-2015.pdf |
| Summary: | In response to the increasing need for automatic and fast satellite image processing SPACE-SI has developed and implemented a
fully automatic image processing chain STORM that performs all processing steps from sensor-corrected optical images (level 1) to
web-delivered map-ready images and products without operator's intervention.
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Initial development was tailored to high resolution RapidEye images, and all crucial and most challenging parts of the planned full
processing chain were developed: module for automatic image orthorectification based on a physical sensor model and supported by
the algorithm for automatic detection of ground control points (GCPs); atmospheric correction module, topographic corrections
module that combines physical approach with Minnaert method and utilizing anisotropic illumination model; and modules for high
level products generation. Various parts of the chain were implemented also for WorldView-2, THEOS, Pleiades, SPOT 6, Landsat
5-8, and PROBA-V. Support of full-frame sensor currently in development by SPACE-SI is in plan.
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The proposed paper focuses on the adaptation of the STORM processing chain to very high resolution multispectral images. The
development concentrated on the sub-module for automatic detection of GCPs. The initially implemented two-step algorithm that
worked only with rasterized vector roads and delivered GCPs with sub-pixel accuracy for the RapidEye images, was improved with
the introduction of a third step: super-fine positioning of each GCP based on a reference raster chip. The added step exploits the high
spatial resolution of the reference raster to improve the final matching results and to achieve pixel accuracy also on very high
resolution optical satellite data. |
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| ISSN: | 1682-1750 2194-9034 |