Compressive Pattern Matching on Multispectral Data
This work addresses pattern detection in multispectral data for remote sensing applications, but it is incremental as it builds on prior minimization techniques.
The paper tackles template and pattern detection in multispectral images using compressive sensing, by adapting an existing L1 minimization method and introducing shifted measurements for reconstruction, with numerical results showing performance across different measurement rates on remotely sensed data.
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 the 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.