Spatial-spectral filtering for the detection of point targets in multi-and hyperspectral data Conference Paper uri icon


  • By using multispectral and hyperspectral data for detecting subpixel targets, we are able to exploit for point target detection both the spectral signature and the overall brightness of the target pixel. The standard methods for such detection (for example, the RX algorithm) assume that an accurate measure of the mean and the covariance matrix of the area is available; the deviation of the suspect pixel from these estimates is a measure of the degree that this pixel is a target. These algorithms are particularly difficult to implement in images which contain multiple areas with different underlying statistical distributions. Such images need local estimates at each pixel to calculate the correct mean and covariance matrix. Even so, edge points between areas will still be incorrectly evaluated both because the pixels themselves are mixtures of different backgrounds and because the local estimate of the …

publication date

  • January 1, 2005