Automatic Extraction of Closed Pixel Clusters for Target Cueing in Hyperspectral Images

摘要

Traditional algorithms for automatic target cueing (ATC) in hyperspectral images, such as the RX algorithm, treat anomaly detection as a simple hypothesis testing problem. Each decision threshold gives rise to a different set of anomalous pixels. The clustered Rx algorithm generates target cues by grouping anomalous pixels into spatial clusters, and retaining only those clusters that satisfy target specific spatial constraints. It produces one set of target cues for each of several decision thresholds, and conservatively requires(Omicron)(K(sup 2)) operations per pixel, where K is the number of spectral bands (which varies from hundreds to thousands in hyperspectral images). A novel ATC algorithm, known as 'Pixel Cluster Cueing' (PCC), is discussed. PCC groups pixels into clusters based on spectral similarity and spatial proximity, and then selects only those clusters that satisfy target-specific spatial constraints as target cues. PCC requires only(Omicron)(K) operations per pixel, and it produces only one set of target cues because it is not an anomaly detection algorithm, i.e., it does not use a decision threshold to classify individual pixels as anomalies. PCC is compared both computationally and statistically to the RX algorithm.