17 May 2017 Multiratio fusion change detection with adaptive thresholding
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Abstract
A ratio-based change detection method known as multiratio fusion (MRF) is proposed and tested. The MRF framework builds on other change detection components proposed in this work: dual ratio (DR) and multiratio (MR). The DR method involves two ratios coupled with adaptive thresholds to maximize detected changes and minimize false alarms. The use of two ratios is shown to outperform the single ratio case when the means of the image pairs are not equal. MR change detection builds on the DR method by including negative imagery to produce four total ratios with adaptive thresholds. Inclusion of negative imagery is shown to improve detection sensitivity and to boost detection performance in certain target and background cases. MRF further expands this concept by fusing together the ratio outputs using a routine in which detections must be verified by two or more ratios to be classified as a true changed pixel. The proposed method is tested with synthetically generated test imagery and real datasets with results compared to other methods found in the literature. DR is shown to significantly outperform the standard single ratio method. MRF produces excellent change detection results that exhibit up to a 22% performance improvement over other methods from the literature at low false-alarm rates.
Patrick C. Hytla, Eric J. Balster, Juan R. Vasquez, Robert M. Neuroth, "Multiratio fusion change detection with adaptive thresholding," Journal of Applied Remote Sensing 11(2), 025010 (17 May 2017). https://doi.org/10.1117/1.JRS.11.025010 . Submission: Received: 7 November 2016; Accepted: 27 April 2017
Received: 7 November 2016; Accepted: 27 April 2017; Published: 17 May 2017
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