A Compositive detection method of lunar crater based on partial Haar and PHOG feature

Abstract

It should adopt a reasonable strategy of selecting features, which are comparatively easy to achieve and can be exactly used for lunar crater detection, and the machine learning methods, which are of high efficiency or of high accuracy, to promote the efficiency of lunar crater detection. We present an integrated method based on the local gray level, the gradient distribution and the machine learning for higher recognition efficiency in high resolution lunar terrain images. The combination of the Haar feature and the AdaBoost classification method provides faster and higher accuracy of crater candidate area detection, and the combination of the local Pyramid Histogram of Oriented Gradients feature and the Support Vector Machine gains an accurate geometric orientation and verification for the candidate craters. It adapts the AdaBoost algorithm as the both feature selection and classification method considering the miscellaneous Haar features. Whereas every item of the Pyramid Histogram of Oriented Gradients feature having influence on classification, it preprocess all crater region image into that each oriented gradient has almost same bins modulus without shadow and highlight pairs. The paper has discussed the mechanism and integration of features selection, classification methods, parameters adjustment and recognition efficacy analysis.