International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 5 – March 2015
A Complete Processing Chain for Shadow Detection
and Reconstruction in VHR Images
Vinaya V. Kulkarni #1, Priyanka S. Borkar #2, Rani S. Kapare #3 Prof. Neelam S. Labhade *4
#
Student , *Prof & E&TC & Savitribai Phule Pune University,India.
1
Vinaya V.Kulkarni @ BE ECE,At Jspm’s ICOER Wagholi,Pune
Priyanka S.Borkar @ BE ECE,ICOER Wagholi, Pune
3 Rani S. Kapare@ BE ECE,ICOER Wagholi,Pune
4
Prof. Neelam S. Labhade @,BE E&Tc, ME in Signal Processig.
2
Abstract—
Building information of automated update in maps from highresolution aerial imagery is one of the most important and
challenging researches in the field of remote sensing and
photogrammetric. Up-to-date building information is needed for
many practical applications such as GIS application analysis,
city planning, fixed assets inventory, etc. Urban land used to
focused on building extraction and height estimation from space
borne optical imagery and study. The merits of such methods is a
GIS databases for decision makers, 3D visualization of urban
areas, and digital urban mapping. In particular, for efficient
building extraction from optical multi-angular imagery, first
essential to eliminate the presence of shadows in very high
resolution (VHR) images, because shadow in VHR can represent
a serious obstacle for their full exploitation. This paper proposes
to face this problem as a whole through the proposal of a
complete processing chain for Shadow Detection and
Reconstruction in VHR images. After, a template matching
algorithm is formulated for automatic relative height and
estimation of the relative building height are utilized in
conjunction with a support vector machine (SVM)-based
classifier for extraction of non-buildings from buildings. The
final results are presented as a building mapand an approximate
3Dimentional model of buildings extraction. The building
detection accuracy of the proposed method is improved to 88%,
compared to 83% without using multi-angular information.
They represent an important problem for both sellers and
users of remote sensing images. As a consequence, shadows
can impact negatively in the exploitation of VHR images,
influencing detailed mapping, leading to erroneous
classification (e.g., biophysical parameters such as water,
vegetation, or soil indexes), due to the partial or total loss of
information in the image.
To remove these demerits and, thus, to increase image
exploitability, two stages are needed: 1) shadow detection and
2) shadow compensation. An example of the importance of
getting shadow-free images is the massive tsunami in 2004
where it was crucial to obtain such images in a very short time
in order to take rapid and crucial decisions in rescue missions
[1].
Index Terms— Image enhancement, image restoration, missing
data, shadow detection, shadow reconstruction, support vector
machines (SVMs), very high resolution (VHR) images.
Fig.1 Illustration of cast and self shadows.
I.
Introduction:
such as the fact that shadow areas have lower
Now a days, very high resolution (VHR) satellite images
opened a new era in the remote sensing field. Because of the
increase of spatial resolution, classification, new analysis and
change detection techniques are needed. Although, VHR
images exhibit resolutions which allow differentiating very
well detailed features from tiny objects, like little building
structures, vehicles, and roofs, trees. Although it is feasible to
explain shadow characteristics to detect building position and
to estimate their height and other useful components usually,
shadows are viewed as undesired information that strongly
affects images.
ISSN: 2231-5381
The former requires primary information about the sensor and
scenario. since, such knowledge is not available, most of the
detection algorithms are based on shadow properties,
brightness higher saturation, , and greater hue values. Other
algorithms rely on the idea of adding characteristics capable to
better discriminate shadow areas (e.g., normalized difference
vegetation index [2], normalized saturation-value difference
index (NSVDI) [3], and maximally stable external regions [4]).
Another technique applies the principal component analysis to
isolate the luminance component in an RGB image, where the
detection of shadows appears more accurate [5]. Finally,
physical properties (e.g., temperature) of a blackbody radiator
have been exploited in a earlier method to detect shadows.
In order to compensate shadow areas, there exist
essentially three different categories : 1) linear correction; 2)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 5 – March 2015
histogram matching; and 3) gamma correlation. The results
obtained with two methods, namely, gamma correction and
linear correlation, are compared. In [6], it is assumed that the
restoration of shadows almost depends on the spectral
signature of the spectral bands.
Accordingly, first, the bands are threshold in an
independent way, calculating the optimal threshold values by
visual inspection. Then, a linear regression in each spectral
band is carried out to correct the shadow effects. In this paper,
an another method is proposed to solve both problems of
detection and reconstruction of shadow areas. Shadow
detection is performed through a hierarchical supervised
classification scheme, while the proposed reconstruction relies
on a linear correlation function, which exploits the
information returned by the classification. The whole
processing chain includes also two important capabilities:
1) a rejection mechanism to limit as much as possible
reconstruction errors
2) explicit handling of the shadow borders.
II.
LITRATURE SURVEY
The detection and removal of shadows have been simulated
using different techniques. A survey of these techniques and
their demerits are discussed in this section. In some
applications, especially inspection system and, traffic analysis
the subsistence of shadows may cause stern nuisance while
segmenting and tracking objects. Shadows can cause object
unification as a consequence of this, shadow detection is
applied to situate the shadow part and discriminate shadows
from foreground objects. Algorithms dealing with shadows
were classified in two-layer taxonomy.
A. Statistical non-parametric (SNP) approach
This approach considers the color constancy ability
of human eyes and exploits the Lambertian
hypothesis (objects with perfectly matte surfaces) to
consider color as a product of irradiance and
repentance. The algorithm is based on pixel modeling
and background subtraction. A statistical learning
procedure is used to automatically determine the
appropriate thresholds.
i.
ii.
iii.
More assumption
Difficult process
Complex
C. Deterministic non-model based (DNM1) approach
It performs object detection by means of a
background suppression and one of its
major novelties is the way the background
model is computed . The model of the
background is defined ad a combination of
statistical and knowledge-based assumptions.
A point of the background assumes the
value (in the RGB color space) that has the
higher probability in a given observation.
Drawback of this approach
i.
ii.
Problem of ghost shadow
Complex
D. Deterministic non-model based (DNM2) approach
This is also a deterministic non-model based
approach, but we have included it because
its capability of detecting penumbra in
moving cast shadow. This approach is one
of the most complete proposed in literature.
Drawback of this approach
i.
ii.
III.
Very complex
Less flexible
BLOCK DIAGRAM:
Drawback of this approach
i.
Less accurate
ii.
Distortion
B. Statistical parametric (SP) approach
Traffic scene shadow detection is an example of
statistical parametric (SP) approach This algorithm
uses two sources of information: local (based on the
appearance of the pixel) and spatial (based on the
assumption that the objects and the shadows are
compact regions).
Fig 2: Block Diagram
Drawback of this approach
ISSN: 2231-5381
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 5 – March 2015
Support Vector Classification-
IV.
The classification problem can be restricted to
consideration of the two-class problem without loss of
generality. In this problem the goal is to separate the two
classes by a function which is induced from available
examples. The goal is to produce a classifier that will
work well on unseen examples, i.e. it generalises well.
Consider the example in Figure 3.Here there are many
possible linear classifiers that can separate the data, but
there is only one that maximises the margin. This linear
classifier is called as the optimal separating hyperplane.
Intuitively, we would expect this boundary to generalise
well as opposed to the other possible boundaries. The key
concept of SVMs, which were originally first developed
for binary classification problems, is the use of
hyperplanes to define decision boundaries separating
between data points of different classes. SVMs are able to
handle both linear, simple, classification tasks, as well as
more complex, i.e. nonlinear, classification problems.
1. Start
2. Satellite image acquisition
3. Binary classification between Shadow and non-shadow
Regions using SVM
4. Morphological Filtering on classified regions
5. Border Creation.
6. Multiclass Classification between following classes
i.
Vegetation
ii.
Soil
iii.
Roads and Buildings
iv.
Ash fault
7. Quality Improvement.
8. Shadow Reconstruction.
9. Pixel Interpolation for smoothing edges.
10. Reconstructed Image.
11. Stop.
V.
Fig.3: Optimal Separating Hyper plane
Both non separable and separable problems are handled by
SVMs in the nonlinear and linear case. The idea behind SVMs
is to map the original data points from the input space to a
high dimensional feature space such that the classification
problem becomes simpler in the feature space.
The basic principle of SVMs is a maximum margin classifier.
Using the kernel methods, the data can be first implicitly
mapped to a high dimensional kernel space. The maximum
margin classifier is determined in the kernel space and the
corresponding decision function in the original space can be
non-linear [2]. The non-linear data in the feature space is
classified into linear data in kernel space by the SVMs. The
aim of SVM classification method is to find an optimal hyper
plane separating irrelevant vectors and relevant by
maximizing the size of the margin.
ISSN: 2231-5381
ALGORITHM
CONCLUSION:
This project has dealt with the important and challenging
problem of reconstruction of VHR images obscured by the
presence of shadows. The proposed methodology is
supervised. The shadow areas are not only detected but also
classified so as to allow their customized compensation. The
classification tasks are implemented by means of the state-ofthe-art SVM approach. A quality check mechanism is
integrated in order to limit misreconstruction problems.
Moreover, borders are explicitly handled by adaptive
morphological filters and linear interpolation for the
prevention of possible border artifacts in the reconstructed
image. In general, from the obtained results, different
considerations may be deduced.
VI.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 5 – March 2015
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index,” in Proc. 18th Int. Conf. Geoinf.,
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[3] H. Ma, Q. Qin, and X. Shen, “Shadow segmentation and
compensation in
high resolution satellite images,” in Proc. IEEE IGARSS, Jul.
2008, vol. 2,
pp. 1036–1039.
[4] H. Y. Yu, J. G. Sun, L. N. Liu, Y. H. Wang, and Y. D.
Wang, “MSER based shadow detection in high resolution
remote sensing image,” in Proc. ICMLC, 2010, pp. 780–783.
Fig: Reconstructed Image
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