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Compressive Pattern Matching on Multispectral Data
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DOI：10.1109/TGRS.2014.2314483.png)
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Abstract
En 中文
We introduce a new constrained minimization problem that performs template and pattern detection on a multispectral image in a compressive sensing context. We use an original minimization problem from Guo and Osher that uses L-1 minimization techniques to perform template detection in a multispectral image. We first adapt this minimization problem to work with compressive sensing data. Then, we extend it to perform pattern detection using a formal transform called the spectralization along a pattern. That extension brings out the problem of measurement reconstruction. We introduce shifted measurements that allow us to reconstruct all measurement with a small overhead, and we give an optimality constraint for simple patterns. We present numerical results showing the performances of the original minimization problem and the compressed ones with different measurement rates and applied on remotely sensed data.
Keywords:
Compressed sensing (CS)
multispectral image
pattern detection
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