Accuracy Dimensions in Remote Sensing

Abstract

ABSTRACT:The technological developments in remote sensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users' needs. The present paper gives the theoretic overview of the issue, besides selected, practice-oriented approaches are evaluated too, finally widely-used dimension metrics like Root Mean Squared Error (RMSE) or confusion matrix are discussed. The authors present data quality features of well-defined and poorly defined object. The central part of the study is the land cover mapping, describing its accuracy management model, presented relevance and uncertainty measures of its influencing quality dimensions. In the paper theory is supported by a case study, where the remote sensing technology is used for supporting the area-based agricultural subsidies of the European Union, in Hungarian administration.

Remote sensing data quality model: from data sources to lifecycle phases

“…The matrix is an excellent base to derive further quality measures. Two basic types exist: the binary and the multiclass confusion matrix (Barsi et al 2018). The binary confusion matrix visualises, how a single class (C) and its complementary (not C) were classified in comparison to the true recognition.…”

“…Based on these items further indicators are to be defined, as PA is the producer's accuracy (recall or true positive rate or sensitivity), UA the user's accuracy (consumer's accuracy or precision or positive predictive value), OA the (overall) accuracy, OE the omission error (false negative rate), CE the commission error (false discovery rate), TNR the true negative rate, FPR the false-positive rate, NPV the negative predictive value and finally FOR the false omission rate. The rate values are often given in per cent (Barsi et al 2018). Confusion matrix can be extended also for multiclass cases.…”

“…Different users reflect a fundamentally different understanding of the subject. To draw a typical example, governmental agencies working with large EO datasets try to understand their most relevant user requirements and develop an official RS data quality scheme that mostly reflects the agency's requirements (Barsi et al 2018). Other key leaders like space agencies from the sensor's side are focusing on the verification, calibration and documentation of uncertainties in data acquisition and processing chain (Masek et al 2013).…”

“…Based on the prior papers of the working group (Batini et al 2017, Albrecht et al 2018, Barsi et al 2018, Kugler et al 2018 this paper aims at giving a brief overview of existing data quality schemes described by standards. Followed by the establishment of a quality model scheme for RS domain based on Batini's concept for information technology domain (Batini and Scannapieco 2016).…”

“…Previous publication of the authors covered case studies of RS data quality (Albrecht et al 2018, Barsi et al 2018, while the current paper aims at giving a general scheme of RS data quality. It is a primary elaboration of a data quality propagation theory, which covers obviously the whole perspective to cover from data acquisition to end-user's requirements.…”