Visible to the public Probability Sampling Protocol for Thematic and Spatial Quality Assessment of Classification Maps Generated From Spaceborne/Airborne Very High Resolution Images

TitleProbability Sampling Protocol for Thematic and Spatial Quality Assessment of Classification Maps Generated From Spaceborne/Airborne Very High Resolution Images
Publication TypeJournal Article
Year of Publication2014
AuthorsBaraldi, A., Boschetti, L., Humber, M.L.
JournalGeoscience and Remote Sensing, IEEE Transactions on
Date PublishedJan
KeywordsAccuracy, categorical variable pair similarity index, Contingency matrix, Decision trees, decision-tree, DigitalGlobe, Earth, error matrix, Estimation, geographic information systems, geophysical image processing, Global Earth Observation System of Systems, Guidelines, image classification, inclusion probability, Indexes, land cover change (LCC) detection, land cover classification, measurement uncertainty, probability, probability sampling protocol, procedural knowledge, Protocols, QA4EO international guidelines, quality assessment, quality assurance, Quality Assurance Framework for Earth Observation guidelines, QuickBird-2 image, remote sensing, sampling methods, Satellite Image Automatic Mapper, SIAM preclassification maps, spaceborne/airborne very high resolution images, Spatial resolution, structural knowledge, subsymbolic object-based spatial quality indicators, symbolic pixel-based thematic quality indicators, thematic maps, WorldView-2 images

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: 1) 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. 2) 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 images, where: 1) an original Categorical Variable Pair Similarity Index (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 2) 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 Mapper (SIAM(tm)) 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 SIAM(tm) by related papers. As a subsidiary conclusion, the statistically consistent and statistically significant accuracy validation of the SIAM(tm) pre-classification maps proposed in this contribution, together with OQIs claimed for SIAM(tm) by related works, make the operational (automatic, accurate, near real-time, robust, scalable) SIAM(tm) 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 initiative and the QA4EO international guidelines.

Citation Key6479283