Sliver Removal in Object-Based Change Detection
from VHR Satellite Images
Luigi Barazzetti
Abstract
This paper presents a novel strategy for object-based change
detection using very high spatial resolution (
VHR
) satellite im-
ages captured under variable off-nadir view angles. The variable
off-nadir angle, along with weak absolute orientation, generates
spurious slivers during the multitemporal comparison of classifi-
cation results. The proposed solution for accurate object-to-object
comparison is based on an intermediate registration of object-
based classification results with a piecewise affine transformation
followed by robust, geometry-based techniques for sliver removal.
Although different remote sensing applications require different
strategies and methods for object-based change detection, the ap-
proach developed in this paper can overcome the overall limita-
tion introduced by the slivers generated by weak geo-localization,
variable off-nadir angles, and image segmentation.
Introduction
Object-based Change Detection from VHR Images
Over the last decade, the availability of very high spatial
resolution satellite images has opened new opportunities in
the fields of photogrammetry and remote sensing (Rogan and
Chen, 2004). Nowadays, multispectral images with sub-meter
spatial resolution can be captured by numerous platforms
characterized by short revisit time (see Belward and Skøien,
2015). Metric accuracy has recently reached 30 cm with the
launch of WorldView-3 in August 2014. Although the metric
resolution is still not comparable with aerial images (
VVHR
,
i.e., very, very high resolution), the continuous acquisition
of
VHR
images is more straightforward than an aerial block
(Barazzetti
et al.
, 2014), which is expensive and requires a
flight plan. Some actual information on aerial cameras and
rules for image acquisition can be found in Kraus (2007).
Traditional pixel-based data processing (Lu
et al.
, 2004;
Radke
et al.
, 2005) is based on the use of the spectral informa-
tion encapsulated into the digital numbers of the image pix-
els. In the case of
VHR
images, pixels are significantly smaller
than real objects (buildings, tree crowns, roads, etc.) and data
processing may evolve from a basic pixel-to-pixel approach
toward a new object-to-object concept defined as Geographic
Object-Based Image Analysis (
GEOBIA
; Hay and Castilla, 2008).
GEOBIA
is based on segmentation of an image to generate
objects (i.e., groups of pixels that are consistent for geometry,
texture, or context). Objects have not only spectral informa-
tion but also spectral statistics (min-max values, mean, stan-
dard deviation, median, etc.), position information (distance
to, center coordinates, etc.) geometric characterization (area,
perimeter, width, height, elliptic fit, etc.), and relationships
with other objects (close to, far from, etc.).
GEOBIA
tools are
already available in several commercial software packages for
remote sensing image analysis, such as eCognition
®
(Definiens
Imaging GmbH, Munich, Germany), ENVI EX module (ITT
Visual Information Solutions, Colorado), ERDAS Imagine
®
Objective module (ERDAS, Inc., Norcross, Georgia), PCI
FeatureObjeX (PCI Geomatics, Ontario, Canada), and IDRISI
Selva (Clark Labs, Massachusetts).
The advent of high spatial resolution remote-sensing
imagery has also provided new opportunities for Object-
Based Change Detection (
OBCD
, see Löw
et al.
, 2015). In
OBCD
,
objects extracted and classified from satellite time series are
compared by using not only spectral information but also
object geometry (Löw
et al.
, 2015). OBIA has a strong connec-
tion to
GIS
data processing. However, one of the fundamental
challenges in
OBCD
concerns the lack of spatial correspon-
dence between objects detected in multi-temporal time series,
due to both geometric and spectral variability. In other words,
objects independently extracted from time series can have dif-
ferent boundaries (Blaschke, 2010) for the reasons illustrated
in the next section.
Main Factors Affecting Object-to-Object Correspondence
Given two
VHR
satellite images, the main factors that affect ob-
ject-to-object correspondence are (a) the variable off-nadir angle,
(b) the overall geo-localization accuracy estimated with (or
without) a bias-compensated Rational Polynomial Coefficient
(
RPC
) camera model (Poli and Toutin, 2012), (c) the resolution of
the Digital Elevation Model (
DEM
) used in orthorectification, and
(d) the segmentation approach for object generation in OBIA.
Shown in Figure 1 is the effect of a variable off-nadir angle
in two QuickBird images. Large off-nadir angles reduce revisit
time, but images lack geometric consistency with terrain-cor-
rected satellite images and other geo-products (maps, spatial
databases, etc.; see Kapnias
et al.
, 2008). In Figure 1b, the sub-
vertical wall exhibits a non-constant spatial displacement of
about 9 m, whereas image spatial resolution is 0.6 m.
Overall geo-localization is another issue of primary im-
portance in which map or geodetic coordinates are related
to the pixels of the image (Oh and Lee, 2015). Orientation
parameters of
VHR
satellite images are provided with a set of
Rational Polynomial Coefficients (
RPC
) derived by the image
data provider from the rigorous model using navigation data.
RPC
s comprise 80 coefficients and allow sensor and camera
model data to remain unrevealed (Fraser
et al.
, 2006; Poli,
2007). However, the direct use of
RPC
s for image orientation
has limitations in determining the true spatial orientation of
every scan line, errors within the direct measurement of sen-
sor orientation (especially attitude), and position and velocity
(Fraser and Hanley, 2003). Sensor orbit and data acquisition
characteristics lead to errors in direct geo-localization signifi-
cantly larger than ground resolution (ground sampling dis-
tance,
GSD
) and require a correction as a bias in image space.
The narrow field-of-view of the satellite line scanner (ap-
proaching a parallel projection for practical purposes) and the
Department of Architecture, Built Environment and
Construction Engineering, Politecnico di Milano, via Ponzio
31, 20133 Milan, Italy (
).
Photogrammetric Engineering & Remote Sensing
Vol. 82, No. 2, February 2016, pp. 161–168.
0099-1112/16/161–168
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.82.2.161
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
February 2016
161
