PNNL

Abstract

The utility of hyperspectral imaging (HSI) has been well established for a wide array of applications but has generated a need for automated screening of high volumes of large HSI cubes. We report two important automated algorithms for efficient standoff processing: atmospheric compensation and target detection. The atmospheric compensation method is based on a fast asymmetric least squares approach that is applied on a pixel-by-pixel basis. The compensation can be applied to entire images without manually identifying regions of interest and utilizes only in-scene information; no ancillary modeling of the atmosphere is required. An iterative target detection approach is also introduced which demonstrates faster speeds relative to moving window approaches. The target detection algorithm classifies each pixel as true target detections, near target detections, clutter, and no-calls. The algorithms were tested using longwave infrared data (7.7 – 11.7 µm) on forty images of twenty-four solid and powder mineral targets placed at a 14-meter standoff distance allowing general observations on expected detection performance for a variety of minerals. In addition to identifying anomalous pixels, the inclusion of “no-calls” significantly reduced the number of false detections.