Automated extraction of image-based endmember bundles of impervious layer using iterative classification strategy

Abstract

Endmember variability associated with impervious layer has been a serious problem in spectral mixture analysis (SMA). A reliable spectral library which ideally models the endmember variability is required for precise SMA. Even though many endmember bundles extraction algorithms have been proposed, there are still some problems in these methods which blur the threshold and endmember numbers. In this paper, an iterative classification extraction endmember bundles algorithm (ICEEA) is proposed. Impervious and pervious training sample are provided with GlobeLand30 product, and Maximum Likelihood Classifier (MLC) is used to conduct iteratively classification. After each classification, the artificial layer pixels which are misclassified as pervious class are excluded from artificial cover, and the impervious sample is selected again in new artificial cover. It stops when there are none misclassified pixels existing in the artificial layer. According to the results of simulated 30m data and real TM data, ICEEA has two advantages over PPI: (1) producing more reliable impervious endmember bundles which can model the endmember variability well; (2) having none threshold setting problem; (2) running much faster than PPI.