Evolutionary Approach for Detection of Buried
Remains Using Hyperspectral Images
León Dozal, José L. Silván-Cárdenas, Daniela Moctezuma, Oscar S. Siordia, and Enrique Naredo
Abstract
Hyperspectral imaging has been successfully utilized to
locate clandestine graves. This study applied a Genetic
Programming technique called Brain Programming (
BP
) for
automating the design of Hyperspectral Visual Attention
Models (
H-VAM.
), which is proposed as a new method for the
detection of buried remains. Four graves were simulated
and monitored during six months by taking in situ spectral
measurements of the ground. Two experiments were imple-
mented using Kappa and weighted Kappa coefficients as
classification accuracy measures for guiding the BP search
of the best
H-VAM.
Experimental results demonstrate that
the proposed
BP
method improves classification accuracy
compared to a previous approach. A better detection per-
formance was observed for the image acquired after three
months from burial. Moreover, results suggest that the use of
spectral bands that respond to vegetation and water con-
tent of the plants and provide evidence that the number of
buried bodies plays a crucial role on a successful detection.
Introduction
Locating unmarked graves represents a complicated and time-
consuming forensic problem because their locations are often
remote and the burial time is generally unknown (Siegel and
Saukko, 2013). The research on the detection of clandestine
graves through multi and hyperspectral images is incipient,
yet has proven to be one of the most challenging forensic
problems. This is an important area of work, since airborne
hyperspectral data enable searching over a large area that is
otherwise inaccessible by foot; especially because, in prin-
ciple, any area of the Earth can be mapped by hyperspectral
imaging, be it with aircraft or satellites (Ross
et al.
, 2005).
Several studies have tested the potential of multispectral
and hyperspectral images with varying results. Kalacska
and Bell (2006) were among the first that demonstrated the
potential of remote sensing as a tool for locating heretofore
unknown mass graves. Afterwards, Kalacska
et al.
(2009)
analyzed the
in situ
and airborne spectral reflectance of a
set of animal mass graves and identically constructed false
graves. Their results indicated that the reflectance spectra
of grave are readily distinguishable from false grave at both
scales. In addition, they observed that vegetation regenera-
tion was severely inhibited by cattle carcasses for up to a
period of 16 months. Caccianiga
et al.
(2012) studied the
effects of decomposition of buried swine carcasses on soil and
vegetation structure and composition as a tool for detecting
clandestine graves. They found that soil disturbance was the
main factor affecting plant cover, while the role of decompo-
sition seemed to be much less critical. Leblanc
et al.
(2014)
performed a blind-test of the potential for airborne hyper-
spectral imaging technology to locate buried remains of pig
carcasses. They were able to predict two single graves, within
GPS
error (10 m), whose location they did not know. Recently,
Silván-Cárdenas
et al.
(2017) studied some methods for de-
tecting clandestine graves using hyperspectral data collected
on ground. Through a controlled experiment using buried
carcasses of pigs, demonstrated that hyperspectral data have
potential for detecting buried remains only after three months
from burial. Furthermore, that the critical spectral regions for
graves detection are the
NIR
and
SWIR1
1
spectral regions, some
of which were so narrow (10 nm) that stressed the need for
hyperspectral sensing.
The method of acquisition of hyperspectral images is equal-
ly important than the process of pattern recognition for detec-
tion of graves based on such information. In this sense, some
techniques of evolutionary computation have been successfully
applied for selection and combination of spectral bands aiming
at different applications such as classification of vegetation spe-
cies, soil mineral identification, synthesizing spectral indices,
estimate pasture mass and quality, and precision farming, to
mention just a few (Ross
et al.
, 2005; Chion
et al.
, 2008; Albar-
racín
et al.
, 2016; Zhuo
et al.
, 2008; Li
et al.
, 2011, Kawamura
et al.
, 2010, Puente
et al.
, 2011, Ullah
et al.
, 2012, Davis
et
al.
, 2006, Landry
et al.
, 2006; Kawamura
et al.
, 2010; Awuley
and Ross, 2016). On the other hand, currently, visual attention
models have been designed for the spatial and spectral analysis
of hyperspectral images with applications such as detection of
prominence, visualization and interpretation, and detection of
objects (Le Moan
et al.
, 2011and 2013; Wang, 2013; Liang
et al.
,
2013; Cao
et al.
, 2015; Zhang
et al.
, 2017).
In this study an evolutionary technique is proposed based
on genetic programming, known as Brain Programming (
BP
),
for optimizing a so-called Hyperspectral Visual Attention
Models (
H-VAM
) for graves detection.
Problem Statement
The present work addresses the problem of detection of clan-
destine graves as a problem of classification of hyperspectral
images. The image classification problem can be stated in for-
mal terms as follows. Suppose we want to classify each pixel
in an image into one of N classes, let say C
1
, C
2
, …, C
N
. Then,
decision rules must be established to enable assignment of
any given pixel to these classes (Varshney and Arora, 2004).
When working with hyperspectral images, some issues
arise due to the high dimensionality of this type of images,
e.g., Hughes phenomenon, high information redundancy in
spectral and spatial domains, need for finding features that
increase discrimination between classes and high computa-
tional resources required in the classification process.
For this reason, a compelling need to reduce the dimension
of data exists. The methods for reduction of dimensionality
can be roughly divided into two categories: feature extraction
Centro de Investigación en Ciencias de Información
Geoespacial, A.C., Circuito Tecnopolo Norte No. 117, Fracc.
Tecnopolo II, Pocitos, Aguascalientes, CP 20313, Mexico
(
).
Photogrammetric Engineering & Remote Sensing
Vol. 84, No. 7, July 2018, pp. 435–450.
0099-1112/18/435–450
© 2018 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.84.7.435
1. The abbreviations used in this paper are summarized in Table 1.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
July 2018
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