AutoSentinel-2/3 (2015 - 2016)


Knowledge-based mapping of Sentinel-2/3 images for operational product generation and content-based image retrieval

The primary objective of the AutoSentinel-2/3 exploratory project is to develop and test a novel remote sensing  (RS) Earth observation (EO) image understanding system architecture (EO-IUS) and implementation suitable for automatic Sentinel-2/3 multi-spectral (MS) image preliminary classification (pre-classification through color space discretization) at large spatial extent in operating mode. The proposed hybrid (combined deductive and inductive) feedback EO-IUS consists of the three following stages.

  • Stage 1. MS image pre-processing (e.g., radiometric calibration) and enhancement (e.g., topographic correction),  provided with feedback loops from the pre-attentive vision Stage 2’s and the attentive vision Stage 3’s categorical outputs. These feedback loops make an inherently ill-posed image enhancement problem (e.g., topographic correction, atmospheric correction, image co-registration, image mosaicking, image compositing, etc.) better posed (conditioned) for numerical treatment. In practice, high-level qualitative (categorical) information, generated at Stages 2 and 3, is employed to stratify (partition) low-level quantitative variables (e.g., sensory data) available as input to Stage 1, in compliance with a divide-and-conquer problem solving principle, regarded as common knowledge.

  • Stage 2. Low-level preliminary classification (pre-classification), where the existing Satellite Image Automatic Mapper (SIAM) software product for prior knowledge-based continuous MS data space discretization is adopted. The sole SIAM’s data requirement is that the input MS image must be radiometrically calibrated into a radiometric physical unit of measure, namely, top-of-atmosphere reflectance or surface reflectance values, where the latter is a special case of the former in clear sky conditions and flat terrain. The SIAM’s output products automatically generated in near real-time from a single-date MS image comprise:

          (i)  multi-level pre-classification maps
          (ii) multi-scale segmentation maps and
         (iii) various “smart” (driven-by-knowledge) spectral indexes, masked (conditioned) by
              spectral categories (e.g., a quantitative greenness index, computed
              as a scalar combination of two or three bands, depending on the image spectral 
              resolution at hand, is masked by a SIAM-based vegetation mask, where all
              available spectral bands are simultaneously considered for spectral categorization 

  • Stage 3. High-level land surface continuous variable (e.g., leaf area index) estimation and categorical variable (e.g., land cover (LC) and LC change class) extraction from single-date and multi-temporal MS images. The extension of the SIAM expert system to MS colour space discretization through time, called SIAM-through-time (SIAMT2) is under development.

Through its experimental activities, AutoSentinel2-/3 will also conceptualize a novel generation of semantic querying systems for content-based image retrieval from multi-source EO “big data” repositories, where the information-as-image-interpretation, available to be queried together with sensory data,  is provided off-line by an automatic EO-IUS incorporating a SIAM/SIAMT2 expert system at Stage 2.

Overall, the present AutoSentinel2/3 exploratory project is conceived as a preparation to a participation in the Horizon 2020 (SPACE, 2016/17) project call.

Instrument: FFG, ASAP (Austrian Space Applications Program), exploratory project

Project volume: 125,580 EUR

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