Zero-mean Minace filters for detection in visible EO imagery

We consider using minimum noise and correlation energy (Minace) filters to detect objects in high-resolution Electro-Optical (EO) visible imagery. EO data is a difficult detection problem because only primitive features such as edges and corners are useful. This occurs because the targets and the background in EO data can have very similar gray levels, which leads to very low contrast targets; no hot spots (present in infrared (IR) data) or bright reflectors (present in synthetic aperture radar (SAR) data) exist in EO data. Since only geometrical (aspect view) distortions are expected in EO data (no thermal variations, as in IR, are expected), we consider using distortion-invariant Minace filters to detect targets. Such filters are shift-invariant and have been shown to be suitable for detection in other data (IR and SAR). Minace filters are attractive distortion-invariant filters (DIFs) because they require only a few filters to handle detection of multiple target classes. These filters must be modified for use on EO data. For EO data, zero-mean Minace filters formed from zero-mean, unit-energy data are used, and thus use of local zero-mean normalized correlations are needed. They show excellent initial detection results.

[1]  Christine M. Netishen,et al.  Performance of a High-Resolution Polarimetric SAR Automatic Target Recognition System , 1993 .

[2]  Deepak S. Turaga,et al.  SAR ship detection using new conditional contrast box filter , 1999, Defense, Security, and Sensing.

[3]  John Rickard,et al.  Adaptive Detection Algorithms for Multiple-Target Situations , 1977, IEEE Transactions on Aerospace and Electronic Systems.

[4]  Abhijit Mahalanobis,et al.  Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set , 1999, Defense, Security, and Sensing.

[5]  Jose C. Principe,et al.  Target prescreening based on a quadratic gamma discriminator , 1998 .

[6]  B. S. Manjunath,et al.  Using texture to analyze and manage large collections of remote sensed image and video data. , 2004, Applied optics.

[7]  H. Oriot,et al.  Building extraction from stereoscopic aerial images. , 2004, Applied optics.

[8]  David Casasent,et al.  Detection filters and algorithm fusion for ATR , 1997, IEEE Trans. Image Process..

[9]  David Casasent,et al.  Detection filters for visible high-resolution imagery , 2003, SPIE Defense + Commercial Sensing.

[10]  Rajesh Shenoy,et al.  Eigen-MINACE SAR detection filters with improved capacity , 1998, Defense, Security, and Sensing.

[11]  D. Casasent,et al.  Minimum noise and correlation energy optical correlation filter. , 1992, Applied optics.

[12]  David Casasent,et al.  Advanced and orthogonal MINACE filter sets: initial detection results , 1994, Defense, Security, and Sensing.

[13]  Rajesh Shenoy,et al.  Synthetic aperture radar detection and clutter rejection minace filters , 1997, Pattern Recognit..