Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Pixel Level Classification Confidence for Remote Sensing Imagery: An Evaluation of Three Interpolation Based Methods

Version 1 : Received: 19 January 2022 / Approved: 24 January 2022 / Online: 24 January 2022 (11:53:46 CET)

How to cite: Jiang, S. Pixel Level Classification Confidence for Remote Sensing Imagery: An Evaluation of Three Interpolation Based Methods. Preprints 2022, 2022010352 (doi: 10.20944/preprints202201.0352.v1). Jiang, S. Pixel Level Classification Confidence for Remote Sensing Imagery: An Evaluation of Three Interpolation Based Methods. Preprints 2022, 2022010352 (doi: 10.20944/preprints202201.0352.v1).

Abstract

Obtaining classification confidence at the pixel level is a challenging task for accuracy assessment in remote sensing image classification. Among the various methods for estimating classification confidence at the pixel level, interpolation-based methods have drawn special attention in the literature. Even though they have been widely recognized in the literature, their usefulness has not been rigorously evaluated. This paper conducts a comprehensive evaluation of three interpolation-based methods: local error matrix method, bootstrap method, and geostatistical method. We applied each of the three methods to three representative datasets with different spatial resolutions, spectral bands, and the number of classes. We then derive the estimated classification confidence and true classification confidence and compared the results with each other using both exploratory data analysis (bi-histogram) and statistical analysis (Willmott's d and Binned classification quality). The results indicate that the three interpolation methods provide some interesting insights on various aspects of estimating per-pixel classification confidence. Unfortunately, the interpolation assumes that classification confidence is smooth across the space, which is usually not true in practice. In other words, interpolation-based methods have limited practical use.

Keywords

Per-pixel classification confidence; spatial pattern; image classification; accuracy assessment; interpolation method

Subject

EARTH SCIENCES, Geoinformatics

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