A Novel Method to Extract Rocks from Mars Images

Wang, Shuliang; Chen, Yasen; Yuan, Huang

doi:10.1049/cje.2015.07.003

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Di et al (2013) adopted a mean-shift segmentation algorithm to generate a set of homogeneous objects and combined 3D point clouds derived from a pair of intensity images to extract both small and large rock candidates. Wang et al (2015) investigated the imagery characteristics of Martian surface and model the interaction between two pixels of an image for differing foreground rocks from background information to keep rover safe navigation. Xiao et al (2017) presented a new autonomous rock detection approach based on homogeneous region-level intensity information and spatial layout.…”

Section: Introductionmentioning

confidence: 99%

A Gradient-Region Constrained Level Set Method for Autonomous Rock Detection From Mars Rover Image

Yang

Zhang

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Rocks are one of the major Martian surface features and yield significant information about the relevant geology process and the life exploration. However, autonomous Martian rock detection is still a challenging task due to the appearance similar to the background, the view and illumination change. Therefore, this paper presents a gradient-region constrained level set method based on mars rover image for automatic Martian rock extraction. In our method, the evolution function of level set consists of the internal energy term for guaranteeing the deviation of the level set function from a signed distance function and the external energy term, where the gradient-based information is integrated with the locally adaptive region-based information, for robustly driving the motion of the zero-level set toward the object boundaries even in images with ununiform grey scale. The resulting evolution of the level set function is based on the minimisation of the overall energy functional using the standard gradient descent method. As a result, those detected Martian surface regions that are most likely to yield valuable scientific discoveries will be further analysed based on two-dimensional shape characterisation. To evaluate the performance of the proposed method, experiments were performed on mars rover image under various terrain and illumination conditions. Results demonstrate that the proposed method is robust and efficient for automatically detecting both small-scale and large-scale rocks on Martian surfaces.</p>

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Section: Introductionmentioning

confidence: 99%