A new approach to detection of sea-mines in sonar imagery that improves the detection density ACF method is presented. The steps are: 1) background normalization, 2) spatially adaptive Wiener filtering, 3) convolution with a 2D FIR filter matched to the target signature, 4) adaptive thresholding to reduce noise, 5) extraction of higher-order spectral features to capture the spatial correlations, 6) extraction of size, strength, and density features, 7) optimal feature selection, and 8) classification. An adaptive Wiener filter is applied to remove noise without destroying the structural information in the mine shapes. The FIR filter is designed to suppress noise and clutter, while enhancing the target signature. A double peak pattern is revealed as the filter passes over highlight and shadow regions. The location, size, and orientation of this pattern can vary. Higher-order spectral features capture the spatial correlations in this pattern and provide invariance to translation and scaling. The approach has been tested on the CSS Sonar 3 database of 60 images with about 84 percent classification accuracy and 11 percent probability of false alarm.