Abstract
The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloud–shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are both conceptual and pragmatic in the definition of remote sensing best practices, which is the focus of efforts made by intergovernmental organizations such as the Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS). First, the ESA EO Level 2 product definition is recommended for consideration as state-of-the-art EO Analysis Ready Data (ARD) format. Second, systematic multi-sensor ESA EO Level 2 information product generation is regarded as: (a) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (b) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2 (proposed as Supplementary Materials), the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed.
Highlights
Radiometric calibration (Cal) is the process of transforming remote sensing (RS) sensory data, consisting of non-negative dimensionless digital numbers (DNs, where DN ≥ 0), provided with no physical meaning, i.e., featuring no radiometric unit of measure, into a physical variable provided with a community-agreed radiometric unit of measure, such as top-of-atmosphere reflectance (TOARF), surface reflectance (SURF) or surface albedo values belonging to the physical domain of change 0.0–1.0 [1,2,3]
To cope with the five Vs characterizing big data analytics, volume, variety, veracity, velocity and value [4], radiometric Cal of Earth observation (EO) big data is considered mandatory by the intergovernmental Group on Earth Observations (GEO)-Committee on Earth Observation Satellites (CEOS) Quality Accuracy Framework for Earth Observation (QA4EO) Calibration/Validation (Cal/Val) guidelines [3]
In a pair of recent surveys about EO image classification systems published in the RS literature in years 2014 and 2016, the word “calibration” is absent [6,7], whereas radiometric calibration preprocessing issues are barely mentioned in a survey dating back to year 2007 [8]
Summary
Radiometric calibration (Cal) is the process of transforming remote sensing (RS) sensory data, consisting of non-negative dimensionless digital numbers (DNs, where DN ≥ 0), provided with no physical meaning, i.e., featuring no radiometric unit of measure, into a physical variable provided with a community-agreed radiometric unit of measure, such as top-of-atmosphere reflectance (TOARF), surface reflectance (SURF) or surface albedo values belonging to the physical domain of change 0.0–1.0 [1,2,3]. A lack of EO input data Cal requirements means that statistical model-based data analytics and inductive learning-from-data algorithms are dominant in the RS community, including (geographic) object-based image analysis (GEOBIA) applications [9,10] in the domain of geographic information science (GIScience). Inductive learning-from-data algorithms are inherently semi-automatic and site-specific [2] They require no radiometric Cal data pre-processing, but they typically gain in robustness when input with radiometrically calibrated data. It comprises: (i) a standard 3-level 8-class DP taxonomy of the Food and Agriculture Organization of the United Nations (FAO) Land Cover Classification System (LCCS) [16], see Figure 1, augmented with (ii) a thematic layer explicitly identified as class “others”, synonym for class “unknown” or “rest of the world”, which includes quality layers cloud and cloud–shadow. Classification NO_DATA SATURATED_OR_DEFECTIVE DARK_AREA_PIXELS CLOUD_SHADOWS VEGETATION BARE_SOILS WATER CLOUD_LOW_PROBABILITY CLOUD_MEDIUM_PROBABILITY CLOUD_HIGH_PROBABILITY THIN_CIRRUS SNOW
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