Automatic extraction of non-bifurcated lunar lineaments from Lunar Reconnaissance Orbiter (LRO) monochromatic images based on a Markov chain method

Abstract

ABSTRACT Lineaments play a critical role in the study of lunar geological evolution. However, lineaments still need to be extracted manually which constrains lunar research. To fill this gap, a Markov chain-based methodology is introduced for automatically extracting vector-based non-bifurcated lineaments from images. The extracted lineaments are formed by successive nodes and line segments, where nodes denote pixel positions and line segments represented connecting lines between nodes. A modified U-net with residual shortcut connections and dilated convolutions is proposed based on the elongated shapes of lineaments to evaluate the probability of nodes belonging to lineaments. Four connection shape features are proposed to describe the connection shapes of nodes and a Gaussian Mixture Model is used to evaluate the probability of line segments belonging to the lineaments based on the proposed features. In the final stage, the lineament probabilities of both nodes and line segments to extract lineaments are considered in the Markov chain. Our method has experimented on a dataset containing 220 samples and 10-fold cross-validation was used to evaluate the performance. Both qualitative and quantitative results indicated that our method can effectively extract non-bifurcated lunar lineaments with arbitrary bending. The method sheds useful light on automatic lineament extraction.