Quality Assessment of Preclassification Maps Generated From Spaceborne/Airborne Multispectral Images by the \textit{Satellite Image Automatic Mapper} and \textit{Atmospheric/Topographic Correction-Spectral Classification} Software Products: Part 1—Theory

Abstract

In compliance with the Quality Assurance Framework for Earth Observation (QA4EO) guidelines, the goal of this paper is to provide a theoretical comparison and an experimental quality assessment of two operational (ready-for-use) expert systems (prior knowledge-based nonadaptive decision trees) for automatic near real-time preattentional classification and segmentation of spaceborne/airborne multispectral (MS) images: the Satellite Image Automatic Mapper™ (SIAM™) software product and the Spectral Classification of surface reflectance signatures (SPECL) secondary product of the Atmospheric/Topographic Correction™ (ATCOR™) commercial software toolbox. For the sake of simplicity, this paper is split into two: Part 1—Theory, presented herein, and Part 2—Experimental results, already published elsewhere. The main theoretical contribution of the present Part 1 is threefold. First, it provides the published Part 2 with an interdisciplinary terminology and a theoretical background encompassing multiple disciplines, such as philosophical hermeneutics, machine learning, artificial intelligence, computer vision, human vision, and remote sensing (RS). Second, it highlights the several degrees of novelty of the ATCOR-SPECL and SIAM deductive preliminary classifiers (preclassifiers) at the four levels of abstraction of an information processing system, namely, system design, knowledge/information representation, algorithms, and implementation. Third, the present Part 1 requires the experimental Part 2 to collect a minimum set of complementary statistically independent metrological quality indicators (QIs) of operativeness (QIOs), in compliance with the QA4EO guidelines and the principles of statistics. In particular, sample QIs are required to be: 1) statistically significant, i.e., provided with a degree of uncertainty in measurement; and 2) statistically valid (consistent), i.e., representative of the entire population being sampled, which requires the implementation of a probability sampling protocol. Largely overlooked by the RS community, these sample QI requirements are almost never satisfied in the RS common practice. As a consequence, to date, QIOs of existing RS image understanding systems (RS-IUSs), including thematic map accuracy, remain largely unknown in statistical terms. The conclusion of the present Part 1 is that the proposed comparison of the two alternative ATCOR-SPECL and SIAM prior knowledge-based preclassifiers in operating mode, accomplished in the Part 2, can be considered appropriate, well-timed, and of potential interest to a large portion of the RS readership.