2012 IEEE International Conference on Robotics and Automation
RiverCentre, Saint Paul, Minnesota, USA
May 14-18, 2012
Autonomous Detection of Volcanic Plumes on Outer Planetary Bodies
Yucong Lin, Melissa Bunte, Srikanth Saripalli and Ronald Greeley
Abstract— We experimentally evaluated the efficacy of various autonomous supervised classification techniques for detecting transient geophysical phenomena. We demonstrated
methods of detecting volcanic plumes on the planetary satellites
Io and Enceladus using spacecraft images from the Voyager,
Galileo, New Horizons, and Cassini missions. We successfully
detected 73-95% of known plumes in images from all four
mission datasets.Additionally, we showed that the same techniques are applicable to differentiating geologic features, such
as plumes and mountains, which exhibit similar appearances
in images.
I. I NTRODUCTION
Spacecraft missions to the outer solar system have identified active transient volcanic eruptive events on multiple planetary bodies (Figure 1). These include large scale
explosive and effusive eruptions on Jupiters satellite, Io,
geyser-like ejections on Saturns satellite, Enceladus, and
Neptunes satellite, Triton, and outgassing of comet nuclei.
These phenomena provide key constraints for subsurface
processes, interior dynamics, surface-interior interactions,
and models of planetary composition [1], [2], [3], [4].
These events cannot be anticipated, thus characterizing them
requires collecting long observation sequences that consume
a large portion of a spacecrafts limited memory capacity and
downlink bandwidth. Such large data volumes preclude the
possibility of sustained surveys of the volcanic activity. The
ability to process observations onboard prior to downlink
could enable long-term monitoring campaigns when used
in conjunction with a method or prioritizing storage and
transmission of data.
We examined uncalibrated spacecraft images of Io and
Enceladus from the Voyager [5], Galileo [6], Cassini [7], and
New Horizons [8] missions. The use of uncalibrated images
was meant to simulate the condition of data onboard the
spacecraft prior to any post-downlink processing that could
enhance the appearance of features. We employed supervised
classification techniques to detect volcanic plumes that were
captured in each image (Figure 11 ). The objective of this
work was to autonomously detect manually verified features
in images under onboard conditions. Success enables these
methods to be applied to future outer solar system missions
and facilitates onboard autonomous detection of transient
events and features regardless of viewing and illumination
effects, electronic interference, and physical image artifacts. Autonomous detection allows the maximization of the
Yucong Lin, Melissa Bunte, Srikanth Saripalli and Ronald Greeley are
with the School of Earth and Space Exploration of Arizona State University.
Tempe,AZ 85287 USA. Yucong.Lin@asu.edu
1 Image numbers correspond to Planetary Data System records. Image
credit: NASA.
978-1-4673-1405-3/12/$31.00 ©2012 IEEE
spacecrafts memory capacity and downlink bandwidth by
prioritizing data of utmost scientific significance.
Plumes in each image of Figure 1 are in limb view;
an additional plume in 1(c) is visible against the surface.
They differ in shape, size, brightness and contrast to their
surroundings. Plumes in Figures 1(b), 1(d) and 1(f) are
brighter due to illumination effects (i.e. viewing angle with
respect to the sun). Without illumination, plumes appear
fainter, as in Figures 1(a), 1(e), 1(g) and 1(h). Plumes
outlined in Figures 1(e) and 1(f) (bottom spare in each
figure; inset in 1(e)) are the same feature but they appear
quite different due to such illumination effects. Image defects
caused by CCD cross talk like the vertical noise stripe in the
same two figures also add to the difficulty of plume detection.
II. R ELATED W ORK
Recently, algorithms for onboard detection of scientific
events have been developed for space missions. Data from
the Mars Exploration Rovers have been examined to detect
dust devils using a method of background subtraction to
reveal motion and to identify clouds using a method of sky
segmentation [9]. Salient surface features of Europa have
been identified in hyperspectral image [10] using a superpixelendmember detection algorithm [11]. Also, an autonomous
method of detecting features beyond the planetary limb of
Io has successfully identified volcanic plumes [12], [13].
Such methods of plume detection involves extraction of
the planetary limb2 using a Hough transform [14] or edge
detection [13] followed by least-squares to fit an ellipse
to the limb. Thereafter, bright pixel regions lying outside
the limb are identified as plumes. This method will fail to
correctly identify the limb in Figure 2(a) and 2(b) because
the strong edges will be identified as limbs, and plumes in
non-limb views such as the lower feature in Figure 1(c) will
not be detected. To overcome these limitations, we combine
feature extraction with supervised classification techniques
to perform plume detection. This method is being applied
to the entire dataset of planetary images where plumes are
known or suspected to exist.
III. M ETHODS
Most known plumes appear bright against the background
of the planetary surface or space and extend for only a few
pixels. In order to determine whether every group of bright
pixels in an image could represent a plume, we applied
Scale Invariant Feature Transform (SIFT) [15] to produce
distinct interest points in every image. SIFT returns feature
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2 the
circumferential edge of the apparent disc of a planet
(a) Galileo 3178r
(c) Voyager c2066612
(e) New Horizons 0035015234
(b) Galileo 5146r
(a) Voyager 2066706
(b)
(c) New Horizons 003512155
(d)
(e) Cassini N00160993
(f)
(d) Voyager C2067145
(f) New Horizons 0035121557
Fig. 3: Sift keypoints produced from our images. Left column: original images; Right
column: keypoints denoted by the green arrows. The directions and magnitudes of the
arrows represent the orientations and scale of keypoints as indicated in [15].
(g) Cassini N00160863
(h) Cassini N00160982
Fig. 1: Sample images of planets and plumes. Plumes are denoted with squares. Some
plumes are enlarged and enhanced for clarity. (a)-(f) are Io. (g)-(h) are Enceladus.
(a) New Horizons 0035092814
(b) Cassini N0016096-5
Fig. 2: Two images where previous approaches will fail. The stripe in (a) is due to
CCD cross-talk. The edge in (b) is Saturns E-ring. (a) is Io and (b) is Enceladus.
Fig. 4: Creation of a keypoint descriptor [15]: 1.)computing the gradient magnitude
and orientation at each image sample point in a region around the keypoint location
(shown on the left). 2.)weighting the samples with a Gaussian window indicated by
the circle. 3.)accumulating the samples into orientation histograms summarizing the
contents over subregions of certain size ( 4x4 for this figure ). In the histogram (right),
the length of each arrow corresponds to the sum of the gradient magnitudes near that
direction within the region. This figure shows a 2x2 descriptor array computed from
an 8x8 set of samples. We use 4x4 descriptors computed from a 16x16 sample array
but we also tested other combinations.
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descriptors for every interest point based on the orientations
of gradients in the local neighborhood of each point (Figure 4, from [15]). The descriptor is invariant to changes
in illumination, image noise, rotation, scaling, and small
changes in viewpoint. It is represented by a 128-dimensional
vector and is appropriate to represent plumes. We randomly
selected 1/3 of the images from each mission dataset as
training images. Random selection ensures that all varieties
of plumes are included in the training sets. After performing
SIFT on training images, we manually categorized feature
descriptors for the presence of plumes: a positive designation
indicates that an interest point is part of a plume; a negative
designation indicates that an interest point is not part of
a plume. Using those training features and categories, we
varied supervised classification techniques to train and test
the classifier on test images. We assessed the results with
different training and testing sets formed by multiple random
selections of images so that the results were independent
of selection. We noted that most keypoints (as shown in
Figure 3) are not from plumes; SIFT features mainly resulted
from surface material gradients and background noise. One
difficulty encountered with supervised classification was the
high ratio of negative features to positive features in training.
For example, in one training set of New Horizons images,
there were 130 positive features and 33,330 negative features.
To classify the features found using SIFT, we employed
K-Nearest Neighbour (KNN) [16], Support Vector Machine
(SVM) [17], and Principle Component Analysis (PCA) [18]
used with KNN.
SVM
decision tree
random forests
KNN
PCA+KNN
larger training set
city block
1st run
22.86%
63.46%
13.46%
70.00%
67.57%
92.71%
81.42%
2nd run
24.73%
66.34%
15.52%
74.29%
69.70%
89.56%
83.54%
TABLE I: Comparison of KNN and other methods on New Horizons. Different rows
conrespond to different randomly selected training sets.
IV. R ESULTS AND A NALYSIS
A. Support Vector Machine
To build a SVM classifier, we used the positive and negative feature classifications from the training set to generate
a decision boundary, a hyper-plane in 128 dimension space
which minimizes the number of features that are not matched
with the correct category. The large number of negative
features makes SVM computationally expensive. In practice,
we built a SVM classifier using a subset of training features
formed by:
• using all positive features and a random selection of
negative features (in this case, 1,000).
• building a SVM classifier with the new training set.
• testing the classifier on the training images and adding
misclassified negative features to the negative feature
sets.
• using the updated negative sets together with all positive
features.
The SVM classifier built using a linear kernel ([17]) for New
Horizons data achieved an unacceptably low detection rate
(Table I) because the positive and negative features could
not be separated by a hyper-plane. Due to the high ratio of
negative to positive features, a decision boundary could only
be generated at the expense of incorrectly classifying most
positive features. Most plume interest points were rejected.
We attempted to reduce this bias using a Radial Basis
Function (RBF) kernel as described in [19]. However, the
results did not improve because the RBF-kernel and linear
kernel can be related with a simple transformation when the
SIFT descriptor is normalized to unit length as in this case.
B. Decision trees and Random forest
A decision tree [20] is a binary tree whose no-leaf node
has only two children and each of them is marked with a
class label. For the SIFT feature descriptor, each node of
the produced decision tree corresponds to a component of
the 128-D vector. A threshold for each node is generated to
separate that component into two classes. The prediction of
each SIFT descriptor starts with the root node. From each
non-leaf node, the procedure goes to the left or right based on
the nodes threshold (if the component corresponding to the
node is less than the threshold, it goes to the left, otherwise,
to the right). This procedure continues until a leaf node is
reached; the class label assigned to the node is the output
of the prediction. Random forest [21] is an ensemble of tree
predictors (ex. decision trees). The random forest classifies
the input feature vector with every tree in the forest and
outputs the class label that received the majority of votes.
Decision trees (Table I) resulted in improved performance
over SVM. Random forests almost failed because each tree
randomly captures only part of a descriptors components
which amounts to loss of information.
C. K Nearest Neighbours
In KNN, a feature vector is classified based upon the
major votes of its neighbours. Euclidean distance is used to
determine its neighbours. KNN tends to be more appropriate
because it does not require particular spatial distribution of
feature vectors and is thus adaptive to the unpredictable
large variety of plumes. We observed that KNN outperforms
a decision tree for New Horizons data (Table I). Decision
tree was inferior because the thresholds assigned to nodes
may be upset by the variety of plumes features. Given
the high ratio of negative to positive features and nonseparability of feature vectors, a feature from a plume will
be surrounded mainly by features that are not from plumes;
therefore the smallest K is intuitively most appropriate to
classify a feature. We found the best results with a K of
1. According to [15], the 128 elements of feature vectors
are formed by a 4x4 array of histograms with 8 orientation
bins in each array (Figure 4). Such a combination is best for
feature discrimination [15]. We tested different combinations
of array sizes on the New Horizons dataset (Table II). We observed that an (8x8)x8 array resulted in the highest detection
accuracy. This is expected as a larger array keeps the most
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feature information. The increase in detection accuracy over
a (4x4)x8 array is not a significant tradeoff for the large
increase in computation time needed for the larger vector
dimensions; therefore, we used the smaller array size.
D. PCA + KNN
Principle Component Analysis is a classical statistical
pattern recognition technique. It reduces data dimension
while maximally maintaining the variation of the data. To
apply PCA to this work as in [18], we:
• formed a data matrix whose rows are SIFT descriptors
and centering the data by subtracting the mean of the
rows.
• computed the singular value decomposition of the centered data matrix A as A = UΣV T .
• used the first 25 columns of V to form a projection
matrix G.
• reduced data dimension by x′ = xG, where x is a
descriptor vector in row.
performed KNN on data after dimension reduction.
PCA+KNN results in a lower detection rate (Table I). This
is expected because every component of the 128-D vector
is equally weighted and reduction of dimension will lose
feature information.
E. Analysis
We found that, of all methods we employed, KNN was
the most successful. KNN allows the most variety in the
appearance of plumes in the training set to be maintained for
classification. Each of the other classification techniques uses
some form information extraction from the training set that
limits the ability of the classifier to distinguish between two
feature classes. We applied SIFT+KNN to all images from
all four missions (Table III). We observed that detection rates
for the Voyager and Cassini datasets were significantly higher
than for the other two mission dataset. We attribute this to
a smaller variety of plume features in these images. Some
detected plumes are displayed in Figures 5 and 6. We found
that the SIFT+KNN method can detect plumes of different
shapes, sizes and orientations. We were able to detect plumes
in noisy images and when bright artifacts or features were
present. Detections failed either because plumes were lost
in glare or classification failed. The misclassification rate
can be reduced by enlarging the training set (Table I, for
New Horizons data) or using another distance measurement
like city-block distance. The city-block distance between
two points is the sum of the absolute difference of their
coordinates. This distance measure produced better results
for New Horizons data (Table I) but did not improve the
results for Galileo data.
Another way to improve the detection rate while reducing
computation time is to use the subset from SVM training
(section IV) in KNN. This resulted in a higher detection
rate at the expense of increased false detection (Table IV).
This tradeoff is acceptable because detecting all plumes is
more important that not detecting obvious features. This
improvement technique allows all plume characteristics to be
identified as positive features; however, one drawback is that
this limits the degree of unknown characteristics that can be
classified as positive features. One goal of this autonomous
processing is to detect and classify unknown features in
future mission observations. Limiting the type of feature
that a classifier positively identifies could preclude new and
geophysically important detections.
In addition to positive plume detections, the methods
employed here allow the classification of other features,
most notably mountain slopes on Io. Mountains on Io are
generally tectonic structures but the process by which they
form is intimately related to volcanism [22]. Distant and low
resolution views do not adequately reveal the presence of
mountains; mountains may appear as bright surface features
similar to plumes viewed against the surface rather than
on the limb. High resolution and more favorable viewing
geometries reveal the texture of mountains against the surface
and/or their topography in limb views. This means that
mountains look similar to plumes against the background
surface and would also be detected as plumes by edge
detection methods because they are high enough to extend
beyond an ellipse that marks the limb. Adding a new feature
classifier to our training set used with KNN resulted in
the accurate detection of mountains despite their similar
appearance to plumes (Figure 8).
Array Size
Detection Rate
(2x2)x8
65.00%
(4x4)x4
72.43%
(4x4)x8
74.29%
(4x4)x16
76.15%
(8x8)x8
79.86%
TABLE II: Detection rate for different combination of histogram arrays and orientation
bins.
Cassini Enceladus
New Horizons Io
Galileo Io
Voyager Io
Number of
test images
23
44
11
81
1st run
Detection
False
rate
detections
93.30%
27
70.00%
55
67.67%
19
95.14%
160
2nd run
Detection
False
rate
detections
88.20%
23
74.29%
63
72.73%
18
92.47%
157
TABLE III: Statistics of plume detection for different image sets
In addition to positive plume detections, the methods
employed here allow the classification and detection of
other features, most notably mountain slopes. Adding a new
feature classifier to our training set used with KNN resulted
in the accurate detection of mountains despite their similar
appearance to plumes. Some results are shown in Figure 8.
Cassini Enceladus
New Horizons Io
Galileo Io
Voyager Io
Number of
test images
23
44
11
81
1st run
Detection
False
rate
detections
98%
167
81%
247
91%
98
95%
1006
2nd run
Detection
False
rate
detections
96%
184
84%
251
83%
103
96%
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TABLE IV: Statistics of plume detection for different image sets with reduced training
set
V. C ONCLUSION AND FUTURE WORK
We have presented a supervised classification approach to
the problem of autonomously detecting plumes in spacecraft
data. Using SIFT as the keypoint detector and feature descriptor and K Nearest Neighbours for feature classification,
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(a) Galileo 3204r
(b) Galileo 5547r
(a) Galileo 3485r
(b) Galileo 4200r
(c) Voyager C2066618
(d) New Horizons 0035860620
(c) Voyager C2066657
(d) Voyager C2067135
(e) New Horizons 0035121557
(f) New Horizons 0035185339
(e) New Horizons 0034974377
(f) New Horizons 0035230642
(g) Cassini N00160965
(h) Cassini W00065147
(g) Cassini N00160861
(h) Cassini N00160995
Fig. 5: Results of SIFT+KNN. Detected plumes are denoted with blue arrows. Some
plumes are enlarged and enhanced for clarity. (a)-(f) are Io. (g)-(h) are Enceladus. The
bright portion of (f) is a view of Jupiter. Io is in the shadow to the left. The bright
feature in (g) is Saturns E-ring. Enceladus is in the background.
this method is able to detect a variety of plumes within
a limited number of images. It achieves, at best, a 93%
detection rate for Cassini images of Enceladus, 74% for New
Horizons images of Io, 73% for Galileo images of Io and
95% for Voyager images of Io. Further, we increased the
detection rate by using SVM to select some of the training
features as the new training set for KNN. Such improvement
does result in more false detections with a smaller training
set, but we are more concerned with accurately detecting
all plumes. This method can also detect mountain slopes on
the planetary surface and differentiate them from plumes.
Future work will include an effort to develop algorithms
for the detection of plume deposits which are not extended
Fig. 6: (continued)Results of SIFT+KNN. Detected plumes are denoted with blue
arrows. Some plumes are enlarged and enhanced for clarity. (a)-(f) are Io. (g)-(h) are
Enceladus.
above the surface but do have characteristics distinct from
the background surface. Plume deposits have been observed
to change over time as new materials are deposited and
old materials are degraded or chemically altered; studying
these changes in appearance have allowed us to monitor
the activity of plumes over the timespan of all spacecraft
missions [23].
R EFERENCES
[1] C. Porco, P. Helfenstein, P. Thomas, et al., Cassini observes the active
south pole of Enceladus, Science 311 (2006) 1393-1401.
[2] R. Lopes-Gautier, A. McEwen, W. Smythe, P. Geissler, L. Kamp,
A. Davies, J. Spencer, L. Keszthelyi, R. Carlson, F. Leader, et al., Active
volcanism on Io: Global distribution and variations in activity, Icarus 140
(1999) 243-264.
3435
(a) New Horizons 0034966580
(b) New Horizons 0035114124
(c) New Horizons 0035140199
(d) Cassini 1487335287
Europa Science Through Onboard Feature Detection In Hyperspectral
Images. Lunar and Planetary Science Conference (2011). Abstract
Number: 1888.
[11] D. R. Thompson, L. Mandrake, M. Gilmore, R. Castano. Superpixel
Endmember Detection. Transactions on Geoscience and Remote Sensing, 48(11):4023-4033, Nov.2010
[12] B. Bue, K. Wagstaff, R. Castano, A. Davies. Automatic Onboard
Detection Of Planetary Vocanism From Images. Lunar and Planetary
Science XXXVIII. 2007. Abstract Number: 1717.
[13] D. R. Thompson, M. Bunte, R. Castano, S. Chien, R. Greeley. Onboard
Image Processing For Autonomous Spacecraft Detection OF Volcanic
Plumes. Lunar and Planetary Science. 2011. Abstract Number: 2433.
[14] R. Duda and P. E. Hart, Use of the Hough Transformation to Detect
Lines and Curves in Pictures, Comm. ACM, 15, 1115 (1972).
[15] D. G. Lowe, Distinctive image features from scale-invariant keypoints.
International Journal of Computer Vision, 60, 2 (2004), pp.91-110.
[16] T. M. Cover, P. E. Hart, Nearest Neighbor Pattern Classification, IEEE
Transansions on Information Theory, 13 (1): 21-27.
[17] C. Cortes, V. Vanpnik, Support-Vector Networks, Machine Learning,
20, 1995
[18] I. T. Jolliffe, Principle Component Analysis. Springer-Verlag.pp.487.
1986.
[19] J. Eichhom, O. Chapelle, Object categorization with svm: kernels for
local features. Advances in Neural Information Processing Systems, 2004
[20] L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone. Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole
Advanced Books & Software,1984.
[21] http://www.stat.berkeley.edu/users/breiman/RandomForests/
[22] E. P. Turtle, W. L. Jaeger, P. M. Shenk, Ionian mountains and tectonics:
Insights into what lies beneath Ios lofty peaks, Io After Galileo: A New
View of Jupiters Volcanic Moon, Lopes, R.M.C., Spencer, J.R. (Eds.)
Springer Praxis. 342pp. (pg. 109-127). 2007.
[23] Geissler, P .E., D. B. Goldstein, Plumes and their deposits, Io After
Galileo: A New View of Jupiters Volcanic Moon, Lopes, R.M.C., Spencer,
J.R. (Eds.) Springer Praxis. 342pp. (pg. 162-192). 2007.
Fig. 7: Plumes lost in glare. (a)-(c) are Io. (d) is Enceladus.
(a) New Horizons 0034829600
(b) New Horizons 0034940539
(c) New Horizons 0034981639
(d) New Horizons 0035040500
Fig. 8: Classification of mountain slopes (green/light arrows) and plumes (red/dark
arrows). (a)-(d) are Io.
[3] B. Smith, L. Soderblom, R. Beebe, J. Boyce, G. Briggs, M. Carr,
S. Collins, A. Cook, et al., The Galilean satellites and Jupiter: Voyager
2 imaging science results, Science 206 (1979) 927.
[4] M. A. Hearn, M. Belton, W. Delamere, L. Feaga, D. Hampton, J. Kissel,
K. Klaasen, L. McFadden, K. Meech, H. Melosh, et al., Epoxi at comet
Hartley 2, Science 332 (2011) 1396.
[5] http://voyager.jpl.nasa.gov/
[6] http://solarsystem.nasa.gov/galileo/
[7] http://saturn.jpl.nasa.gov/index.cfm
[8] http://pluto.jhuapl.edu/mission/whereis nh.php
[9] A. Castano, A. Fukunaga, J. Biesiadecki, L. Neakrase, P. Whelley,
R. Greeley, M. Lemmon, R. Castano, S. Chien. Automatic detection of
dust devils and clouds on Mars. Machine Vision and Applica- tions(2008)
19:467-482
[10] M. Bunte, D. R. Thompson, R. Castano, S. Chien, R. Greeley. Enabling
3436