1017
AUTOMATED ROCK SEGMENTATION
FOR MARS EXPLORATION ROVER IMAGERY
Yonghak Song
School of Civil Engineering, Purdue University
West Lafayette, IN 47906, USA - songlO@purdue.edu
Commission IV WG 7
KEY WORDS: Active Contour, Edge Flow, Feature Extraction, Level Set Method, Mars, Multi-resolution, Segmentation, Texture
ABSTRACT:
Rock segmentation is important for the success of the Mars Exploration Rover mission and its scientific studies. In this paper, a
framework for automated rock segmentation using texture-based image segmentation and edge-flow driven active contour is
developed and implemented. Three schemes: wavelet based local transform, multi-resolution histograms, and inter-scale decision
fusion are combined and applied for texture-based image segmentation. The result is refined by active contour based on level set
method, which is propagated in the edge flow vector field. Test images taken by the panorama and navigation cameras on the rover
Spirit at the Gusev Crater landing site are used in this study. This paper presents the theory, implementation, and the test results
along with discussions on the performance of the proposed method.
1. INTRODUCTION
The Mars Exploration Rover (MER), Spirit and Opportunity
have collected a large amount of Mars surface imagery since
their arrival on Mars in 2004. As the most important tasks of the
MER mission, route planning and geologic analysis demand the
identification of observed rocks. For route planning, rocks must
be detected before producing rock maps at the landing sites. In
terms of geologic and planetary science, rocks might hold the
clues to past water activity and carry important information
about environmental characteristics and processes.
Rock segmentation in an image is essential for rock mapping.
Currently, rock segmentation in MER imagery is mostly
accomplished by manual labelling which is extreme time
consuming and tedious. Further more, the increasing amount of
data being collected by the rovers or similar missions makes
manual operation impractical and automated solution demanded.
In addition, automated rock segmentation is also needed as part
of the on-board processing. Improvement in the mobility and
lifespan of MER allows for more images to be collected than
the capability of the outer space communication bandwidth to
transmit to the Earth. This fact highlights a crucial demand for
effective data compression schemes that can prioritize regions
in an image based on their scientific values. Automated rock
segmentation will benefit such on-board data compress schemes
(Roush et al., 1999).
To meet these needs on automated rock segmentation, this
study presents an automated solution consisting of two stages:
texture-based image segmentation as initials and active contours
based boundary refinement. For the texture-based image
segmentation, three texture analysis approaches are used: multi
channel approach, multi-resolution histogram, and inter-scale
decision fusion. These three approaches are integrated and
embedded into a framework for rock detection using discrete
wavelet transforms. This texture-based image segmentation can
roughly segment the rocks in the MER images, but can not
yield satisfactory rock boundaries. To resolve this problem, the
initial boundaries are refined by means of active contours based
on the level set method. This boundary refinement allows us to
achieve not only finer boundaries but also topologically correct
rock segmentation results. Finally, the suggested framework for
automated rock detection is applied to Mars surface images
collected by MER PANCAM using various filters and
NAVCAM, all at the Gusev Crater landing site.
The rest of this paper is organized as follows. Section 2 briefs
the previous work for automatic rock extraction, while Section
3 explains the proposed methodology and describes the detail
process. Presented in Section 4 are our implementation and its
results on MER images. The paper concludes in Section 5 with
the evaluation about the properties and performance of the
proposed method with perspectives on future efforts. .
2. RELATED WORK
There have been a number of efforts towards automatic rock
extraction from imagery. For mining studies, Crida and Jager
(1994) propose a knowledge-based approach for rock
recognition from imagery, which consists of two parts. The first
part includes three stages: blob edge detection, boundary
completion, and blob extent calculation. The second part
involves testing the hypothesis, where the classification of
interesting regions detected as blobs in the first part is
performed according to twelve rule-based features. Initially, the
feature vectors are classified by thresholding and then the
remaining vectors initially rejected as non-rocks are reclassified
using a supervised k-nearest neighbour classification. Although
it is one of the good initial efforts for rock detection, it suffers
from heavy computation and the difficulty of threshold
determination. Gilmore et al. (2000) show that rock is texturally
distinctive features and can be detected successfully in Mars-
like desert pavement environment since rocks differ
significantly from soils in terms of texture. They use Gabor-
filter for texture feature extraction and maximum-likelihood
method for classification. They focus on general strategy rather