Baraldi, Andrea; Boschetti, Luigi; Humber, Michael (2013): Probability sampling protocol of classification maps from spaceborne/airborne image [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.806528,

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Supplement to: Baraldi, A et al. (2013): Probability sampling protocol for thematic and spatial quality assessments of classification maps generated from spaceborne/airborne very high resolution image. IEEE Transactions on Geoscience and Remote Sensing, 51(9), https://doi.org/10.1109/TGRS.2013.2243739

To deliver sample estimates provided with the necessary probability foundation to permit generalization from the sample data subset to the whole target population being sampled, probability sampling strategies are required to satisfy three necessary not sufficient conditions: (i) All inclusion probabilities be greater than zero in the target population to be sampled. If some sampling units have an inclusion probability of zero, then a map accuracy assessment does not represent the entire target region depicted in the map to be assessed. (ii) The inclusion probabilities must be: (a) knowable for nonsampled units and (b) known for those units selected in the sample: since the inclusion probability determines the weight attached to each sampling unit in the accuracy estimation formulas, if the inclusion probabilities are unknown, so are the estimation weights. This original work presents a novel (to the best of these authors' knowledge, the first) probability sampling protocol for quality assessment and comparison of thematic maps generated from spaceborne/airborne Very High Resolution (VHR) images, where: (I) an original Categorical Variable Pair Similarity Index (CVPSI, proposed in two different formulations) is estimated as a fuzzy degree of match between a reference and a test semantic vocabulary, which may not coincide, and (II) both symbolic pixel-based thematic quality indicators (TQIs) and sub-symbolic object-based spatial quality indicators (SQIs) are estimated with a degree of uncertainty in measurement in compliance with the well-known Quality Assurance Framework for Earth Observation (QA4EO) guidelines. Like a decision-tree, any protocol (guidelines for best practice) comprises a set of rules, equivalent to structural knowledge, and an order of presentation of the rule set, known as procedural knowledge. The combination of these two levels of knowledge makes an original protocol worth more than the sum of its parts. The several degrees of novelty of the proposed probability sampling protocol are highlighted in this paper, at the levels of understanding of both structural and procedural knowledge, in comparison with related multi-disciplinary works selected from the existing literature. In the experimental session the proposed protocol is tested for accuracy validation of preliminary classification maps automatically generated by the Satellite Image Automatic MapperTM (SIAMTM) software product from two WorldView-2 images and one QuickBird-2 image provided by DigitalGlobe for testing purposes. In these experiments, collected TQIs and SQIs are statistically valid, statistically significant, consistent across maps and in agreement with theoretical expectations, visual (qualitative) evidence and quantitative quality indexes of operativeness (OQIs) claimed for SIAMTM by related papers. As a subsidiary conclusion, the statistically consistent and statistically significant accuracy validation of the SIAMTM pre-classification maps proposed in this contribution, together with OQIs claimed for SIAMTM by related works, make the operational (automatic, accurate, near real-time, robust, scalable) SIAMTM software product eligible for opening up new inter-disciplinary research and market opportunities in accordance with the visionary goal of the Global Earth Observation System of Systems (GEOSS) initiative and the QA4EO international guidelines.

Overlapping area matrices between:

(A) the QuickBird-like Satellite Image Automatic MapperTM (Q-SIAMTM) preliminary classification maps at fine, intermediate, coarse semantic granularity (52, 28 and 12 spectral categories) generated from three very high resolution (VHR) test images: WorldView-2 T1, WorldView-2 T2, QuickBird-2 and

(B) Reference thematic samples belonging to 7 land cover classes, selected by Michael Humber in the three VHR test images.

Quality indicators of an Overlapping area matrix: Overall accuracy, Producer's accuracy, User's accuracy, Categorical Variable Pair Similarity Index.

Files contain three test maps each: Q-SIAMTM at fine, intermediate, coarse granularity; One reference land cover class set: 7