This article was downloaded by:
On: 26 May 2010
Access details: Access Details: Free Access
Publisher Taylor & Francis
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 3741 Mortimer Street, London W1T 3JH, UK
International Journal of Remote Sensing
Publication details, including instructions for authors and subscription information:
http://www.informaworld.com/smpp/title~content=t713722504
Feature extraction for Darfur: geospatial applications in the documentation
of human rights abuses
John J. Sulika; Scott Edwardsb
a
Department of Geography, The Florida State University, Tallahassee, FL, USA b Amnesty
International USA, Washington, DC, USA
Online publication date: 20 May 2010
To cite this Article Sulik, John J. and Edwards, Scott(2010) 'Feature extraction for Darfur: geospatial applications in the
documentation of human rights abuses', International Journal of Remote Sensing, 31: 10, 2521 — 2533
To link to this Article: DOI: 10.1080/01431161003698369
URL: http://dx.doi.org/10.1080/01431161003698369
PLEASE SCROLL DOWN FOR ARTICLE
Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf
This article may be used for research, teaching and private study purposes. Any substantial or
systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or
distribution in any form to anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contents
will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses
should be independently verified with primary sources. The publisher shall not be liable for any loss,
actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly
or indirectly in connection with or arising out of the use of this material.
International Journal of Remote Sensing
Vol. 31, No. 10, 20 May 2010, 2521–2533
Feature extraction for Darfur: geospatial applications in the
documentation of human rights abuses
JOHN J. SULIK*† and SCOTT EDWARDS‡
†Department of Geography, The Florida State University, Tallahassee, FL 32306, USA
‡Amnesty International USA, Washington DC 20003, USA
Downloaded At: 18:27 26 May 2010
(Received 15 February 2009; in final form 19 November 2009)
Geospatial technologies are rapidly becoming adopted by advocates of human
rights abuses, engaged in non-governmental monitoring. Limited available funding puts constraints on the amount of time staff can perform Geographic
Information System (GIS) and remote-sensing tasks. Therefore, semi-automated
techniques are forthcoming in order to facilitate data-analysis tasks aimed at
sharing information about violent conflict and human rights abuses. As a contribution to these efforts, this paper details classification of a pre-conflict image,
binary partitioning of the classification results, application of morphological
filters, estimation of total number of pre-conflict structures and overlay of the
refined information onto the post-conflict image for damage assessment in Darfur,
Sudan. We present a novel application of geospatial technologies and imageprocessing techniques aimed at expediting the dissemination of critical information necessary to inform the public and policy makers of detailed multi-temporal
analyses of evidence of human rights abuses.
1.
Introduction
The task of collecting information during an active conflict is one that frequently
stymies effective human rights monitoring and humanitarian planning. International
human rights organizations, such as Amnesty International and Human Rights
Watch, as well as local and regional organizations, have been collecting testimony
and ground-level observational data on abuses since the outbreak of hostilities in
Sudan’s westernmost provinces in early 2003. Despite the best efforts of these organizations, the immense size of Darfur, combined with persistent insecurity, has made
systematic and comprehensive documentation of potential abuses all but impossible.
Furthermore, and in response to escalating international outcry over the government’s counterinsurgency tactics, the Government of Sudan (GoS) has severely
limited access to Darfur for international human rights organizations, media and
even humanitarian aid providers.
The effects of survey limitations are evident when considering the death toll of
Darfur as of late 2009, described as ranging from 200 000 to over 400 000 by disparate
studies in which the same operational definitions were used. Alternatively, the GoS
claims that no more than 9000 died as a result of the conflict. Moreover, the GoS
claims that departures from this estimate are exaggerations by ‘Western media and
NGOs’ (Non-governmental organizations; Reuters-Alertnet 2007). While this
*Corresponding author. Email: jsulik@fsu.edu
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online # 2010 Taylor & Francis
http://www.tandf.co.uk/journals
DOI: 10.1080/01431161003698369
Downloaded At: 18:27 26 May 2010
2522
J. J. Sulik and S. Edwards
Figure 1. Huts with conical roofs typically have a wide-range spectral response. The southeast
portion of each hut often reflects high amounts of electromagnetic radiation with the northwest
portion in shade.
particular claim is not widely viewed as credible, the inconsistent estimations raise a
pressing question: when information concerning human rights abuses is difficult to
attain in a particular area, what geospatial tools can be useful for advocates and
policy-makers to circumvent the data limitation?
Here, we explore the use of satellite image feature extraction in the case of Darfur,
with particular emphasis on the technical aspects of systematic data extraction, to
determine the number of extant dwellings in villages throughout the region. In doing
so, we present a method for using limited observation data of a semi-arid region that is
generally available to non-governmental organizations with reduced technical
and material capability. This method employs established image-processing
methods to overcome the inherent limitations of distinguishing indigenous housing
structures from their surrounding environment. Specifically, pixel-based operations
are employed to answer image-segmentation difficulties caused by spectral overlap
(figure 1). The goal of this study is to determine the feasibility of accurately quantifying the number of huts within a given village. Furthermore, we intend to demonstrate
the general applicability of such methods to the systematic collection of data for
purposes of humanitarian aid provision and/or abuse documentation and human
rights advocacy.
2.
2.1
Background
Previous research
Many researchers have investigated the application of remotely sensed imagery to
humanitarian efforts (Bjorgo 2000, Brown et al. 2001, Giada et al. 2003). Giada et al.
Downloaded At: 18:27 26 May 2010
Feature extraction for Darfur
2523
(2003) automated tent extraction for a refugee camp in Tanzania, and as is the case in
many studies, the dwellings analysed were of a distinct shape and easily distinguished
from surrounding material. Giada’s study employed high-resolution IKONOS imagery in which dwelling units were clearly identified; however, the authors decided that
classification of pixels into area classes was not as effective as object-oriented techniques or morphological operators (Giada et al. 2003). Unfortunately, specialized
software such as eCognition is prohibitively expensive for many researchers and
non-profit humanitarian organizations and requires extensive training beyond basic
image-processing skills, thus rendering it impractical for non-specialists to implement.
While the human rights application put forth here is novel, there are existing
Geographic Information System (GIS) methods and applications for humanitarian
purposes. For example, Prins (2008) performed change-detection analysis of Landsat
Enhanced Thematic Mapper Plus (ETMþ) data from a conflict-affected area of
Darfur in order to identify burned villages. Although useful for a medium-resolution
(30 m) approach, new problems become apparent when examining the burned villages
at a finer scale. Finer scale approaches often seek to isolate individual structures from
remotely sensed imagery, as opposed to an entire village. Mathematical morphology
has been applied in many remote-sensing studies, some of which are similar in application to the one proposed here (Soille and Pesaresi 2002, Giada et al. 2003). The main
distinction between the application explored in this article and most of the existing
applications in the literature is the difficulty in segmenting the study area so that
features of interest are isolated. Specifically, refugee camps are typically composed of
dwelling units that are easily distinguished from background features and have clear
outlines, whereas most of the villages inhabited by ethnic Africans in Darfur are made
of local materials and are not easily distinguishable from the surrounding area.
Darfur villages are comprised of compounds with lineaments that visibly intersect
dwelling units. Moreover, huts in Darfur villages do not exhibit a homogeneous
spectral response across their surface, a property exhibited by dwelling units in
other studies (Mason and Fraser 1998, Bjorgo 2000, Ruther et al. 2002). These are
formidable barriers to data sampling (Lo 1995, Giada et al. 2003, De Laet et al. 2007).
Many studies often quantify structures in informal settlements through photogrammetric techniques. These procedures rely on Digital Surface Models (DSMs) derived
from imagery acquired through air surveys (Mason and Fraser 1998, Ruther et al.
2002) to use an elevation component to enhance object identification; however, the
security situation in Darfur precludes this approach.
2.2
The conflict in Darfur
In the summer of 2003, following a series of defeats by armed opposition groups in
Darfur, the GoS began pouring military resources into Darfur and the surrounding
areas, heavily arming the Janjawid as a paramilitary force. These government-enabled
militias initiated a counter-insurgency campaign that relied on the destruction of communities from which the rebels originated. As the number of groups taking up arms in
Darfur expanded, the targeting of civilians quickly spread to the rest of the country.
The government-enabled militias quickly gained the upper hand against the Darfur
rebel movements, and by spring 2004, thousands of people, mostly civilians, had been
killed, and over a million people had been forcibly displaced. While over the course of
the conflict, the Darfur-armed opposition groups were responsible for many human
rights violations, the militia forces backed by GoS units systematically depopulated
2524
J. J. Sulik and S. Edwards
large swaths of Darfur through the use of terror tactics. Anything that made life
possible was targeted for destruction; however, burnt domiciles are the most apparent
evidence in commercially available satellite sensor images.
3.
Downloaded At: 18:27 26 May 2010
3.1
Data
The case of Darfur: goals of and obstacles to automated data collection
Although geospatial technology is increasingly accessible to advocacy organizations,
there remain many encumbrances involved with remotely sensed imagery depicting
evidence of human rights abuses. First, the pre-conflict and post-conflict images are
rarely captured on anniversary dates. As a result, seasonal differences in soil moisture,
biomass, atmospheric variation and burn scars left after an attack complicate change
detection. The location of remotely observable conflict is rarely known prior to an
event and acquisition of a pre-conflict image can be subject to availability within the
archives of data providers such as DigitalGlobe. When reasonable pre- and postconflict images are accessible, the burden of analysis on resource-limited organizations is still substantial. Structures in a village number from the hundreds to well into
the thousands, making it infeasible to hand count them. This difficulty provided the
impetus to develop a method for automated and systematic assessment of dwellings.
Feature extraction within each village can only be successful if each free-standing
structure can be distinguished as a discrete object. If this criterion is met, the information class that represents all of the huts in a scene can be vectorized and converted to a
shapefile. The attribute table for each shapefile will have one record for each contiguous group of pixels. The attribute table can then be appended with additional
fields and analysed within a GIS. A brief characterization of the study area will be useful
for method description. Groups of huts are usually surrounded by and touching grass
walls and fences with a similar spectral response. Moreover, most dwelling units of
sedentary farming communities have roofs and fences made out of local thatch. This
causes features of interest to be grouped in the same class as background information,
such as grass and shrubs. In addition, the fences are often so close to dwelling units that
they appear to be connected when viewed from high-resolution satellite imagery. This
becomes problematic when trying to isolate dwelling units from fences and brush based
on digital number values of pixels. Further difficulty arises because the huts in each
village are small and have conical thatch roofs. These roofs cause light to scatter in
multiple directions, often resulting in heterogeneous pixel values for each hut. This
makes it difficult to spectrally identify each hut as a homogeneous object because up to
half of each roof falls in the shadow of the other half. To emphasize this, all pixel values
that are not representative of huts in the red band have a standard deviation of 25.96
with a range of 255 (table 1). In contrast, the standard deviation of pixel values for huts
in the red band is 78.06 with a range of 255 (table 2).
Table 1. 2004 pixel statistics for background.
Basic statistics
Band 1
Band 2
Band 3
Band 4
Minimum
Maximum
Mean
Standard deviation
1
1
1
1
160
190
255
246
100.79
132.70
192.76
168.92
13.07
17.89
25.96
22.08
Feature extraction for Darfur
2525
Table 2. 2004 pixel statistics for huts.
Basic statistics
Band 1
Band 2
Band 3
Band 4
Downloaded At: 18:27 26 May 2010
3.2
Minimum
Maximum
Mean
Standard deviation
1
1
1
1
165
199
255
242
55.15
65.17
91.51
70.87
47.16
58.23
78.06
70.88
Data sources
Standard Quickbird images of Jonjona village in North Darfur, Sudan were processed
using ENVI 4.2 and the MATLAB Image Processing Toolbox. This set of satellite
sensor imagery was kindly provided by the American Association for the Advancement
of Science Geospatial Technology and Human Rights (GaTHR) program. The village
of Jonjona (figure 2) was reportedly attacked on 07 May 2006 when militias burned 16
houses within the village (American Association for the Advancement of Science 2009).
Due to image-availability restrictions, the pre-conflict image was captured on 07
December 2004 and the post-conflict image was captured on 23 February 2007; see
table 3 for characteristics of the images used in this study.
Digital image-processing procedures were performed using ENVI. Shapefiles
created with ENVI were opened in ArcMap for final analysis. Accuracy assessment
was performed using manually digitized shapefiles (provided by the American
Association for the Advancement of Science’s Human Rights Program and
Figure 2. False-colour infrared image of Jonjona village. Bands 4, 2 and 1 correspond to
wavelengths of 0.76–0.89 m (near-infrared), 0.52–0.60 m (green) and 0.45–0.52 m (blue),
respectively.
2526
J. J. Sulik and S. Edwards
Table 3. Parameters of the Quickbird images used in this study.
Date
View angle
07 December 2004
23 February 2007
Ground sampling
distance (m)
Pan-sharpening DRA Map projection
4.7
–
0.611
–
4 band
4 band
Yes
Yes
UTM zone 35N
UTM zone 35N
Geospatial Technologies and Human Rights Project) indicating the number and
location of destroyed structures.
4.
Downloaded At: 18:27 26 May 2010
4.1
Methods
Classification procedures
The goal of this study is to determine the feasibility of accurately quantifying the
number of huts within a given village. As a consequence, the spatial extent of this
analysis was set by delineating a region of interest (ROI) around the village perimeter.
Furthermore, every feature in the scene besides a hut is considered background noise.
Specifically, the classification goal was to sort all image pixels into two classes, one
representing huts and the other representing all other features in the scene.
Classification of the entire scene is unnecessary because the location and number of
huts in each image is the only information that is normally used for human rights
advocacy purposes. When comparing huts (table 2) to all other information content
(table 1) in the 07 December 2004 image, both categories have similar ranges of pixel
values across all spectral bands; however, the features of interest (huts) have larger
standard deviation values, owing mostly to the conical shape of hut roofs.
Another impediment to classification was the Dynamic Range Adjustment (DRA)
option (table 3) that was applied by the data vendor. Unfortunately, images with
DRA are not recommended for spectral classification (DigitalGlobe 2009). To deal
with this, a simple non-parametric classification procedure was used to group pixels
between spectral bands. The ISODATA algorithm was chosen because of the nonnormal distribution of pixel values and poorly defined surface features within the
scene (Jensen 2007). The ISODATA algorithm treats the entire dataset as one cluster
and decomposes it into a number of natural spectral clusters after iterating through
the dataset in a self-organizing way.
In order to better evaluate this procedure, the unsupervised ISODATA technique
was compared to two supervised classification techniques. Training data was collected in order to prepare a Maximum Likelihood Classification. Training pixels were
collected across the entire study area, with over 300 pixels used for each of five classes.
This sample size was chosen in accordance with the principle that each sample should
minimally be 30 times the number of features in the dataset (Mather 2004).
Unfortunately, the histograms for each training set were extremely non-normal,
and this was further aggravated by the large standard deviation of the pixels in the
training classes for huts and vegetation, thus causing the Maximum Likelihood
classifier to perform very poorly. A Support Vector Machine (SVM) was then used
because it is non-parametric and suitable for heterogeneous spectral classes. Instead of
relying on statistical criteria for class membership, SVM classifiers exploit geometric
criteria based on maximizing the margin between two classes (Melgani and Bruzzone
Downloaded At: 18:27 26 May 2010
Feature extraction for Darfur
2527
2004). Although smaller training samples could have been suitable for the SVM (Foody
and Mathur 2004), the training sites used for the Maximum Likelihood classification
were reused for this analysis. The SVM classifier implemented in this study used the
pairwise classification strategy for multi-class classification. The parameters were
determined by trial analyses and a radial basis function kernel with a Gamma of 0.30
was used in conjunction with a penalty parameter of 90. The results of the SVM
classification appear in table 4. Although the overall accuracy of the SVM classification
is rather good, the spectral class corresponding to huts and fencing still had multiple
errors and did not transfer well to the rest of the study. Because of this, only the
ISODATA results for the hut and fencing spectral class were further analysed.
ISODATA was initially set to iterate to seven clusters, and attempts were made to
recode the clusters into hut/non-hut classes, yet the results were unsatisfactory.
Repeated trial analyses determined that 80 clusters were optimal for recoding groups
of pixels into the two desired classification categories. It was necessary to break the
scene into many spectral clusters (using ISODATA) and then iteratively label the
resulting spectral classes, as ‘hut’ is an information class that is extremely heterogeneous and significantly overlaps with the spectral classes that occur for bare soil,
tree shadow, hut shadow, fencing and glare resulting from illumination. Each cluster
was interactively highlighted to determine correspondence to features of interest or
background information, ultimately being recoded into a binary image.
Binary images contain pixels that fall within two object sets: set A is the foreground
(Boolean 1) and set B is the background (Boolean 0). In this study, village structures
and all other pixels with significant spectral overlap are in set A and all other pixels are
grouped into set B (figure 3). Generalization of pixel values into two groups has been
successfully used to quantify features within remotely sensed imagery (Glasbey et al.
1991, Laliberte and Ripple 2003) and prepares the image for transformations based
on set theory.
4.2
Removal of interfering features
Morphological operators are based on set theory and are similar to smoothing filters.
However, unlike convolution filters that act on spectral properties, morphological
filters modify the spatial properties of foreground pixels (set A) relative to background pixels (set B). Morphological transformations are applied through a structural element that dictates the connectivity (topology) of pixel groups from set A that
are allowed to pass through the filter. The connectivity within these structural
elements defines what information is retained from the original image (Serra and
Vincent 1992).
Table 4. Accuracy assessment of Support Vector Machine classification of entire village.
Class
Hut/fence
Tree
Grass
Soil
Overall accuracy ¼ 94.7784%
Kappa coefficient ¼ 0.8998
Producer
User
Commission (%) Omission (%) accuracy (%) accuracy (%)
33.21
0
0.63
1.77
12.86
8.26
28.51
0
87.14
91.74
71.49
100
66.79
100
99.37
98.23
Downloaded At: 18:27 26 May 2010
2528
J. J. Sulik and S. Edwards
Figure 3. Morphological filtering sequence with (a) huts and compound fencing as set A, (b) a
binary image where set A is white and set B is black, (c) erosion of set A by set B and (d) dilation
of set A in order to approximately expand the huts back to their original size.
This procedure has been extended to greyscale images (Sternberg 1986); however,
the current study only considers the application of morphological operators to binary
images. The two basic morphological operators are dilation and erosion and are
defined in equations (1) and (2), respectively,
X ¯B ¼ fxjBx ˙ X Þ ;g
(1)
X @B ¼ fxjBx X g
(2)
and
for a binary image X and a structural element B where Bx ¼ fb þ xjb 2 Bg is the
translation of B at point x. Dilation operators fill holes smaller than the structural
element and add pixels to object boundaries, whereas erosion operators remove pixels
from object boundaries. Additional operators can be implemented by combining
dilation and erosion. Closing operators are a combination of a dilation operator
followed by an erosion operator, essentially filling in gaps and removing narrow
features. Lastly, the opening operator is the reverse of a closing operator (Serra and
Vincent 1992). For an in-depth treatment of mathematical morphology, see Destival
(1986), Haralick et al. (1987) and Soille (2003).
Downloaded At: 18:27 26 May 2010
Feature extraction for Darfur
2529
The heterogeneous spectral response of image objects (see figure 2) underscores the need
for advanced processing techniques. For instance, similar colours are shared by tree
shadows and hut shadows, portions of hut roofs and bright patches of soil, and between
the compound fencing and hut roofs. Separation of these features based on pixel value
alone is intractable; therefore, the binary image was eroded then dilated. As detailed in
figure 3, erosion is the dilation of set B and removes all features that do not correspond to
the parameters of the structural element, thus eliminating interfering features within the
remotely sensed image. In order to expand the huts back to their original shape, the
erosion operator was followed by the dilation operator, which erodes set B according to
the presence of foreground pixels within the structural element. This study uses 5 5 discshaped structural elements because the features of interest are circular. Theoretically, any
stray pixels or (linear) features smaller than the size of the structural element are eroded.
An area threshold of 30 m was used to eliminate features larger than typical structures, and the resulting file was overlain on the original image (figure 4). Therefore,
figure 4(b) represents a combination of the original image and the refined geographic
information that has been teased out of it in order to draw emphasis to dwelling units.
It is evident that overlay analysis makes it easier to count features in the pre-conflict
image by providing analysts with immediate visual cues regarding features of interest.
In addition, overlay analysis was also applied in this manner to the post-conflict
image to help discern destroyed structures (figure 4). Since the geographic information from the pre-conflict image was carried over into the post-conflict image, this
overlay procedure provides an elementary type of change detection. Specifically, this
form of multi-temporal analysis provides information on pre-existing structures that
have been obscured by burn scars and the passage of time, thus overcoming limitations to interpreting change pairs via side-by-side comparison.
5.
Results and discussion
The utility of combining geographic data with remotely sensed imagery to aid in postdisaster assessment has proved to be quite successful in other studies. However, this
paper differs in that the overlay procedure described herein uses generalized information from the before image instead of overlaying ancillary geographic layers such as
population data.
Figure 4. Overlay analysis of extracted detail onto (a) pre-attack image and (b) post-attack image.
2530
J. J. Sulik and S. Edwards
Table 5. Accuracy assessment of pre-conflict image feature extraction.
Proportion
Accuracy (%)
Identification accuracy
Errors of commission
Errors of omission
284/424
66.9
86/424
20.3
52/424
12.8
Downloaded At: 18:27 26 May 2010
Given the inherent limitations in the dataset, the results are favourable (table 5). In
an effort to improve feature extraction accuracy, more advanced morphological
techniques were attempted. Specifically, morphological opening by reconstruction
(Soille 2003) was performed in an attempt to better preserve the shape of the manmade structures; however, this was unsuccessful because much of the fencing around
each compound was contiguous with huts. As a result, this led to ‘over-reconstruction’. Specifically, the mask image allowed most of the fencing to be reconstructed
after the original binary image was eroded by a 5 5 disc-shaped structural element
(figure 5). The fact that this technique should logically improve feature boundaries yet
Figure 5. Morphological reconstruction sequence showing (a) the binary image, (b) the
morphological erosion with a 5 5 disc-shaped structuring element, (c) the opening by
reconstruction that demonstrates how many smaller features were not reconstructed, yet
fencing contours are still apparent, and (d) dilation of eroded image.
Feature extraction for Darfur
2531
has the opposite effect, only places further emphasis on the difficulties imposed by the
spectral overlap between huts and compound fencing.
Downloaded At: 18:27 26 May 2010
5.1
Quantitative accuracy evaluation
The accuracy of the hut extraction method was evaluated quantitatively by the use of
several accuracy indicators. Identification accuracy, error of omission and error of
commission were chosen because of their use in other feature extraction studies (Shi
and Zhu 2002, Zhu et al. 2005). Manually digitized vector files of huts were used for
the accuracy assessment. An omitted structure is one that was misclassified as background and a committed structure is background information that was misclassified
as a structure. Errors were calculated based on the number of huts correctly extracted
rather than estimated per-pixel (Congalton 1991, Giada et al. 2003). Errors of omission resulted from tree shadows that have the same spectral response and are the same
size and shape as hut shadows, making it difficult to distinguish between the two.
Commission errors can be attributed to the difficulty in isolating tree shadows and are
caused by the same technical difficulties as the errors of omission. Improvement of
commission errors could possibly be attained with object-oriented methods; however,
it would be more time effective to manually remove committed structures.
Although not fully automated, the procedure presented here addresses the need of
multi-temporal quantitative analysis of pre/post-conflict imagery. This methodology
significantly improves the feature extraction process considering the amount of
spectral overlap features of interest and background features (figure 2). The described
methodology of change observation via overlay analysis can be used by human rights
practitioners to aid in assessing the extent of damage that occurs in conflict areas,
considering that some settlements contain thousands of structures. Since visual
analysis of pre- and post-conflict imagery without image-processing techniques is
time-prohibitive, application of the proposed techniques can be used to expedite the
dissemination of critical information regarding evidence of human rights abuses.
6.
Conclusion: current and future applications
There is clearly a need for rapid data collection and analysis methods with minimal
cost for human rights groups. The techniques presented would prove useful for most
semi-desert landscapes where it is difficult to separate individual settlement structures
from surrounding features. For instance, the circular hut with a thatch roof is
prevalent across African countries and can be observed from in desert climates,
such as Chad, all the way to tropical climates found in places such as Burma. The
methods presented here describe the role remote-sensing tools and analysis techniques
can have in documenting the effects of militarized conflict on civilian centres and
allow for accurate assessment of the scope and scale of damage, with significantly
decreased costs associated with data collection. These techniques presented would
prove most useful for most semi-desert landscapes where it is difficult to separate
individual settlement structures from surrounding features.
The method presented in this paper exemplifies that the need for cost minimization
adds to the growing body of exemplars of the roles remote-sensing tools and analysis
techniques can have in documenting the effects of militarized conflict on civilian
centres. The steady decline in imagery costs, combined with the rapid growth of the
archives offers NGOs a valuable new tool. As demonstrated, however, mere collection
2532
J. J. Sulik and S. Edwards
of this imagery without access to techniques that minimize the costs associated with
systematic analysis may make the successful use of the imagery still too costly.
Acknowledgements
The authors would like to thank the referees for the careful review and valuable
comments which provided insight that helped to improve the paper and Lars Bromley
for providing geospatial data, and to recognize Tingting Zhao, Elise Gornish, Juliette
Rousselot and William H. Moore for their input and valuable research assistance.
Downloaded At: 18:27 26 May 2010
References
BJORGO, E., 2000, Using very high spatial resolution multispectral satellite sensor imagery
to monitor refugee camps. International Journal of Remote Sensing, 21, pp.
611–616.
AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE, 2009, High-resolution satellite
imagery and the conflict in Chad and Sudan. Available online at: http://shr.aaas.org/
geotech/darfur/darfur.shtml (accessed 10 January 2009).
BROWN, V., JACQUIER, G., COULOMBIER, D., BALANDINE, S., BELANGER, F. and LEGROS, D., 2001,
Rapid assessment of population size by area sampling in disaster situations. Disasters,
25, pp. 164–171.
CONGALTON, R.G., 1991, A review of assessing the accuracy of classifications of remotely sensed
data. Remote Sensing of Environment, 37, pp. 35–46.
DE LAET, V., PAULISSEN, E. and WAELKENS, M., 2007, Methods for the extraction of archaeological features from very high-resolution Ikonos-2 remote sensing imagery, Hisar
(southwest Turkey). Journal of Archaeological Science, 34, pp. 830–841.
DESTIVAL, I., 1986, Mathematical morphology applied to remote sensing. Acta Astronautica, 13,
pp. 371–385.
DIGITALGLOBE, 2009, Quickbird Imagery Products Guide (Longmont, CO: DigitalGlobe).
FOODY, G.M. and MATHUR, A., 2004, Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of
Environment, 93, pp. 107–117.
GIADA, S., DE GROEVE, T., EHRLICH, D. and SOILLE, P., 2003, Information extraction from very
high resolution satellite imagery over Lukole refugee camp, Tanzania. International
Journal of Remote Sensing, 24, pp. 4251–4266.
GLASBEY, C.A., HORGAN, G.W. and DARBYSHIRE, J.F., 1991, Image analysis and 3-dimensional
modeling of pores in soil aggregates. Journal of Soil Science, 42, pp. 479–486.
HARALICK, R.M., STERNBERG, S.R. and ZHUANG, X.H., 1987, Image analysis using mathematical morphology. IEEE Transactions On Pattern Analysis And Machine Intelligence, 9,
pp. 532–550.
JENSEN, J.R., 2007, Remote Sensing of the Environment: An Earth Resource Perspective, 2nd edn
(Upper Saddle River, NJ: Prentice Hall).
LALIBERTE, A.S. and RIPPLE, W.J., 2003, Automated wildlife counts from remotely sensed
imagery. Wildlife Society Bulletin, 31, pp. 362–371.
LO, C.P., 1995, Automated population and dwelling unit estimation from high-resolution
satellite images – a GIS approach. International Journal of Remote Sensing, 16, pp.
17–34.
MASON, S.O. and FRASER, C.S., 1998, Image sources for informal settlement management.
Photogrammetric Record, 16, pp. 313–330.
MATHER, P.M., 2004, Computer Processing of Remotely Sensed Images: An Introduction, 4th edn
(Chichester, UK: Wiley).
Downloaded At: 18:27 26 May 2010
Feature extraction for Darfur
2533
MELGANI, F. and BRUZZONE, L., 2004, Classification of hyperspectral remote sensing images
with support vector machines. IEEE Transactions on Geoscience and Remote Sensing,
42, pp. 1778–1790.
PRINS, E., 2008, Use of low cost Landsat ETM to spot burnt villages in Darfur, Sudan.
International Journal of Remote Sensing, 29, pp. 1207–1214.
REUTERS-ALERTNET, 2007, Sudan’s Bashir defends record in Darfur. Available online at: http://
www.alertnet.org/thenews/newsdesk/L24114026.htm (accessed 23 February 2009).
RUTHER, H., MARTINE, H.M. and MTALO, E.G., 2002, Application of snakes and dynamic
programming optimisation technique in modeling of buildings in informal settlement
areas. ISPRS Journal of Photogrammetry and Remote Sensing, 56, pp. 269–282.
SERRA, J. and VINCENT, L., 1992, An overview of morphological filtering. Circuits Systems and
Signal Processing, 11, pp. 47–108.
SHI, W.Z. and ZHU, C.Q., 2002, The line segment match method for extracting road network
from high-resolution satellite images. IEEE Transactions on Geoscience and Remote
Sensing, 40, pp. 511–514.
SOILLE, P., 2003, Morphological Image Analysis: Principles and Applications (Berlin, Germany:
Springer-Verlag).
SOILLE, P. and PESARESI, M., 2002, Advances in mathematical morphology applied to
geoscience and remote sensing. IEEE Transactions on Geoscience and Remote Sensing,
40, pp. 2042–2055.
STERNBERG, S.R., 1986, Grayscale morphology. Computer Vision Graphics and Image
Processing, 35, pp. 333–355.
ZHU, C., SHI, W., PESARESI, M., LIU, L., CHEN, X. and KING, B., 2005, The recognition of road
network from high-resolution satellite remotely sensed data using image morphological
characteristics. International Journal of Remote Sensing, 26, pp. 5493–5508.