Application of automated iterative target detection for standoff hyperspectral imaging
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Eigenvector Research, Inc., Manson, WA (United States)
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 more efficient standoff processing: atmospheric correction and target detection. The atmospheric correction method is based on a fast asymmetric least squares approach that is applied on a pixel-by-pixel basis. Here, the correction 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 on forty images of twenty-two solid 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” reduced the number of false detections significantly.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-76RL01830
- OSTI ID:
- 2575615
- Report Number(s):
- PNNL-SA--200130
- Journal Information:
- Journal of Applied Remote Sensing, Journal Name: Journal of Applied Remote Sensing Journal Issue: 01 Vol. 19; ISSN 1931-3195
- Publisher:
- SPIECopyright Statement
- Country of Publication:
- United States
- Language:
- English
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