Context-dependent detection via Alarm-Set Fusion and segmentation

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Taylor C. Glenn, Brandon Smock, Joseph N. Wilson, Paul D. Gader

Published: 2013, Last Modified: 07 Nov 2025WHISPERS 2013EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: A COntext-DEpendent (CODE) detection method is described. Detectors are run in separate image segments, and the detector outputs are aggregated to produce a system output. The process of aggregating outputs is called Alarm-Set Fusion (ASF). A Run Packing (RP) ASF technique is described for training an ASF algorithm. The RP ASF algorithm uses dynamic programming to optimize area under the Receiver Operating Characteristic (ROC) curve. Experimental results are presented using real hyperspectral images segmented using hyperspectral and LiDAR features. Context-dependent and -independent Hybrid Sub-pixel Detector (HSD) and Adaptive Cosine Estimator (ACE) detectors were compared. The CODE method yielded better performance. A comparison to an ACE-RX detector was conducted. Detection performance was similar but the CODE computational requirements are lower. Experiments also show that the ASF algorithm can learn non-obvious threshold combinations.