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Accordingly, the challenge is to render the existing array of ground and satellite based input atmospheric optical measurements coherent, standard and available. Physical coherency and standardization are accomplished by
assimilating the atmospheric optical measurements and their expected errors with a micro-physical atmospheric model driven by measured, meteorological-scale wind fields. Such models act at least to the measurement community as
intelligent interpolators in space and time, and as a tool for product quality assurance and standardization.
8.3.2.5 Atmospheric Sounding
Atmospheric sounding provides a vertical distribution of atmospheric parameters such as temperature, pressure, and composition, e.g, aerosols, using data from a sensor above the atmosphere. The atmospheric profile is a derived
image.
8.3.2.6 Pixel Fusion
Combine remote sensing data with other sources of geospatial information to improve the understanding of specific phenomena. Fusion Levels: I-GRSS reference
Pixel level fusion - Data Fusion
8.3.3 Imagery Metadata
Metadata is data about data ISO 19115. It is a schema required for describing geographic information and services. It provides information about the identification, the extent, the quality, the spatial and temporal schema, spatial reference,
and distribution of digital geographic data.
ISO 19115-2 Geographic Information – Metadata – Part 2 Extensions for imagery
8.3.4 IG_Image application specialization
Editors note: 19101-2 WD1 defines a conceptual schema for IG_Image. IG_Image is a specialization of grid coverage from 19123. There certainly are application-specific specializations of IG_Image, e.g., for SPOT image, for Landsat
image, for MODIS image, etc. Perhaps this should be stated in 19101-2? A later volume 19109-2 could define rules for specialization of IG_Image for applications.
Also consider product specification of types of IG_Image
8.3.5 Encoding rules for imagery
Editors note: develop this section using NWIP for Geographic Information – Encoding – Part 2 - Encoding Rules Imagery and Gridded Data.
The information encoded depends upon the purpose of encoding: long-term archive vs. rapid distribution, visualization vs. processing, etc.
A multi-tiered approach to metadata is shown graphically in Figure 13
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Figure 13 - Multi-tiered imagery encoding
8.4 Geographic imagery knowledge – inference and interpretation
8.4.1 Knowledge from
imagery
Knowledge is an organized, integrated collection of facts and generalizations. Imagery based knowledge is accumulated by systematic study and organized by general principles; mathematics is the basis for much scientific
knowledge. One aspect of moving from information to knowledge is the identification of redundancies. Knowledge differs from data or information in that new knowledge may be created from existing knowledge using logical inference.
Knowledge is more than a static encoding of facts, it also includes the ability to use those facts in interacting with the world.
Figure 14 provides shows the image knowledge packages. Each package is addressed in the following clauses
Figure 14 – Image knowledge packages
8.4.2 Image understanding and classification
Processing of imagery is one method to identify a collection of named features, where the features are of types identified by in a feature catalogue See Figure 6.
Interpretation of image is a semiotic process. Sensors provide partial information about phenomena occurring in the environment. From this source of information, regions in an image can be aggregated under a single concept, i.e. a
named feature. The process moves raw sensed data to higher semantic content information, e.g., polygonal coverages. This process may also be called image understanding: knowledge-based interpretation of visual scenes by
computers. IG_ImageValuesMatrix Encoding
IG_Image Encoding MD_ Metadata Encoding
Imagery Dataset Encoding
