An Unsupervised Urban Change Detection
Procedure by Using Luminance and Saturation
for Multispectral Remotely Sensed Images
Su Ye and Dongmei Chen
Abstract
Unsupervised change detection techniques have been widely
employed in the remote-sensing area when suitable reference
data is not available. Image (or Index) differencing is one of the
most commonly used methods due to its simplicity. However,
past applications of image differencing were often inefficient in
separating real change and noise due to the lack of steps for fea-
ture selection and integration of contextual information. To ad-
dress these issues, we propose a novel unsupervised procedure
which uses two complementary features, namely luminance
and saturation, extracted from multispectral images, and com-
bines T-point thresholding, Bayes fusion, and Markov Random
Fields. Through a case study, the performance of our proposed
procedure was compared with other three unsupervised change-
detection methods including Principle Component Analysis
(
PCA
), Fuzzy c-means (
FCM
), and Expectation Maximum-Markov
Random Field (
EM-MRF
). The change detection results from our
proposed method are more compact with less noise than those
from other methods over urban areas. The quantitative accu-
racy assessment indicates that the overall accuracy and Kappa
statistic of our proposed procedure are 95.1 percent and 83.3
percent, respectively, which are significantly higher than the
other three unsupervised change detection methods.
Introduction
There is a growing interest in monitoring land-use/land-cover
change as it provides up-to-date information for many appli-
cations. Employing remote-sensing (RS) technology has been
critical for keeping track of land-use/land-cover transition at
a variety of spatial scales (Rogan and Chen, 2004; Hussain
et
al.
, 2013). Compared with traditional monitoring methods
(such as field surveying), RS-based change detection can bet-
ter allow for processing large areas, producing quantitative re-
sults and offering repeatable procedures (Coppin
et al.
, 2004).
Numerous state-of-the-art approaches have been developed
to analyze RS imagery for change detection. These methods
are usually categorized into supervised and unsupervised
methods, according to the availability of adequate reference
data (Bruzzone and Prieto, 2000; Bruzzone and Prieto, 2002;
Fernandez-Prieto and Marconcini, 2011). The advantage of
supervised change detection is the capability of labeling the
type of change (the detailed “from-to” information) based on
given training samples. However, the generation of suitable
multi-temporal reference data to characterize all the classes
is usually a difficult task, especially for historical images (Lu
et al.
, 2004). Compared with supervised methods, unsuper-
vised ones can be much more cost-effective since no reference
data is required. In spite of being unable to offer the informa-
tion on categories of land transition, the changed/no-change
detection is often acceptable for many practical applications
(Hussain
et al.
, 2013).
Image differencing (or index differencing) is one of the
most commonly used methods for unsupervised change
detection (Bruzzone and Prieto, 2002; Rogerson, 2002; Lu
et
al.
, 2004). Compared with other unsupervised approaches,
such as Principle Component Analysis (Deng
et al.
, 2008) or
clustering algorithms (Bruzzone and Prieto, 2000), image dif-
ferencing is much cheaper computationally, and it is easier to
interpret its results (Lu
et al.
, 2004; Hussain
et al.
, 2013). The
basic idea for image differencing stems from the fact that the
physical status of land area can be characterized by certain
feature indices derived from the remotely sensed data; when
we analyze targeted features from bi-temporal images, the
larger its deviating values from means of unchanged class
appear to be, the more likely it is that change has occurred in
the corresponding area. The useful features for image differ-
encing can be defined as digital number in a single spectral
band, vegetation indexes (Singh, 1989), principle component
(Deng
et al.
, 2008), or texture index (Tomowski
et al.
, 2010).
Feature-differencing values of interested areas are usually
passed to a thresholding strategy to separate “no-change” and
“changed” class for the final result map.
However, image or index differencing often exhibits incon-
sistent performances, as it makes its decision relying only on
single feature analysis. For most urban change-detection tasks,
when single feature differencing is applied, we may have (a)
real change information corresponding to transition between
different land-cover types which are usually of interest, and
(b) noisy change identification due to other factors, such as
seasonal growth or local illumination variance. In the compli-
cated practical scenes, clusters of real and noisy changes are
sometimes mixed together in the feature space; thus, we are
unable to completely separate them by using a single thresh-
olding value. In this sense, fusion techniques merging multiple
difference images have been introduced to improve detection
accuracy (Le Hégarat-Mascle and Seltz, 2004; Du
et al.
, 2012),
since different features might offer complementary informa-
tion about the patterns to be classified (Kittler
et al.
, 1998) .
The second issue with traditional image differencing is
that global analysis of difference image fails to account for
local spatial information influencing the reliability of final
result. To address this issue, one solution is incorporating the
direct difference of certain texture indices for change detec-
tion (Li and Leung, 2002; Tomowski
et al.
, 2010). Another
method is applying Markov Random Fields (MRFs) models
(Bruzzone and Prieto, 2000; Kasetkasem and Varshney, 2002;
Zhang
et al.
, 2007; Benedek and Szirányi, 2009), which has
Department of Geography, Queen’s University, Kingston,
Ontario, K7L 3N6 (
).
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 8, August 2015, pp. 637–645.
0099-1112/15/637–645
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.8.637
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
August 2015
637
