We consider two problems: first, the problem of detection of objects in images of 3D planetary terrain; second, the task of finding corresponding points for stereo matching of this type of images. We propose an approach that is simultaneously applicable to both problem areas. The approach uses a bank of filters based on different 2D Gabor functions. By detection we mean locating multiple classes of targets with distortions present and in a cluttered background. It is desirable to minimize false alarms due to clutter, image noise, and the presence of other objects. In the scenario of stereo matching, the pixel location where we search for the corresponding point is the target, while all ambiguous matches are non-targets. In this work, we use Gabor filter banks in two versions. First, for fast detection of targets, the single filter outputs of the bank are fused by linear combination. Second, for stereo matching, the outputs of the filters form a feature vector used to find the best match. We refer to both types of filters as a macro Gabor filter. In the linear combination case, the filter bank forms a single filter. This filter is correlated with an input image, followed by local maximum detection, and thresholding to yield the finally detected targets. The new aspects are: combining real and imaginary parts of GFs into one filter using centered on off-center GFs, separately optimizing the fusion coefficients of the GFs by controlling the shape of the correlation outputs of each filter alone, and the application to two different scenarios.