Abstract
Automatic feature identification from orbital imagery would be of wide use in planetary science. For geo scientific applications, automatic shape-based feature detection offers a fast and non-subjective means of identifying geological structures within data. Most previously published examples of circular feature detection for geo scientific applications aimed to identify impact craters from optical or topographic data. Various techniques used include the texture analysis, template matching, and machine learning. In this paper, we propose a new method for the extraction of features from the planetary surface, based on the combination of several image processing techniques, including a shadow removal, watershed segmentation and the Circular Hough Transform (CHT). The original edge map of craters is detected by canny operator. In most literatures Hough transform is generally used for crater detection but we have added a shadow removal which includes a novel color image fusion method, based on the multi-scale Retinex (MSR) and discrete wavelet transform (DWT), is proposed. This proposed method is capable of detecting partially visible craters, and overlapping craters.
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Tamililakkiya, V., Vani, K. (2011). Feature Extraction from Lunar Images. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Digital Image Processing and Information Technology. DPPR 2011. Communications in Computer and Information Science, vol 205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24055-3_4
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DOI: https://doi.org/10.1007/978-3-642-24055-3_4
Publisher Name: Springer, Berlin, Heidelberg
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