This paper addresses a specific example of nonperiodic translation symmetry and presents an algorithm to automatically detect multiple poles, or their shadows, in aerial imagery by looking for consistent and overlapping regions of self-similarity across a non-urban scene. The algorithm does not rely on having a pole template or knowing its exact size. For each image patch, similar regions (or blobs) are found across the whole image using normalised cross-correlation. The blobs are filtered based on the known orientation of the pole and its approximate size. The algorithm then clusters together all image patches that have a large number of mutually overlapping blobs, indicating a common degree of self-similarity between them. For non-urban scenes, these are likely to identify similar shaped poles. Results on actual aerial imagery demonstrate the algorithm's potential to detect most poles with few false alarms, and shows superior performance to a template-matching approach.