International Journal of Geosciences, 2012, 3, 21-24 Published Online February 2012 (
Complex Object Shapes Recognition. Automatic Aid
Photointerpretation in a Satellite Image
Kada Mouedden1, Youcef Amar1, Macho Anani2, Sara Lebid1, Mohammed Benyahia1
1Department of Envir o n me n t a l Sciences, Faculty of Science, Djillali Liabes University of
Sidi Bel Abbes, Sidi Bel Abbes, Algeria
2Department of Electronics, Faculty of Engineering Sciences, Djillali Liabes University of
Sidi Bel Abbes, Sidi Bel Abbes, Algeria
Received February 18, 2011; revised April 15, 2011; accepted June 23, 2011
The interpretation of geological structures on earth observatio n images involves like man y other domains to both visual
observation as well as specialized knowledge. To help this process and make it more objective, we propose a method to
extract the components of complex shapes with a geological significance. Thus, remote sensing allows the production
of digital recordings reflecting the objects’ brightness measures on the soil. These recordings are often presented as im-
ages and ready to be computer automatically processed. The numerical techniques used exploit the morphology mathe-
matical transformation s properties. Presentation shows the operations’ sequen ces with tailored properties. The example
shown is a portion of an anticline fraction in which the organization shows clearly oriented entities. The results are ob-
tained by a procedure with an interest in the geological reasoning: it is the extraction of entities involved in the observed
structure and the exploration of the main direction of a set of objects striking the structure. Extraction of elementary
entities is made by their physical and physiognomic characteristics recognition such as reflectance, the shadow effect,
size, shape or orientation. The resulting image must then be stripped frequently of many artifacts. Another sequence has
been developed to minimize the noise due to the direct identification of physical measures contained in the image. Data
from different spectral bands are first filtered by an operator of grayscale morphology to remove high frequency spatial
components. The image then obtained in the treatment that follows is therefore more compact and closer to the needs of
the geologis t. The sear ch for sign ifican t overall di rection comes from interception measures sampling a rotation from
0 to 180 degrees. The results obtained show a clear geological significance of the organization of the extracted ob-
Keywords: Object Shapes Recognition; P h o tointerpr et a t i on
1. Introduction
Photointerpretation uses the intuitiv e and deductive skills
of photo interpreter and includes processes that allow
him to define the studied object’s color as its radiometric
value, optical density, grayscale, shape, size, environment
and spatial and spectral relationships with other objects
(Texture, Structure). Thus, a direct correspondence be-
tween the observed shape in the image and of the object
on the ground is performed: it is the identification. For
each geological, geomorphologic using remote sensing,
the number of spatial images to interpret becomes in-
creasingly important. It is therefore necessary to develop
aid tools for interpretation. Automatic recognition, with
computer aided design of geological structures and geo-
morphological featu res o n many image s is a goal t hat fi ts
into this framework. However, a major difficulty for nu-
merical simulation of a photo-geological reasoning is to
preserve the interpreted properties in a continuous space
by models applied to the discrete space of the digital im-
age. In particular, textural elements in obvious visual
interpretation may be discrete entities in the image. The
reasoning model must preserve this “thematic connec-
tion” of the interpreter [1].
2. Principle of Complex Pattern Recognition
When the radiometric or local texture cannot recognize
an object its shape is often used as a discriminator. In ad-
dition, in the case of geology, the forms that constitute a
structure in a natural environment are generally complex.
In remote sensing, identification of these structures is
usually done visually: the photo-interpretation used op-
erates images that has undergone treatment for improve-
ment but does not directly call for specialized methods
opyright © 2012 SciRes. IJG
The main posed problem is then the objective localiza-
tion of a complex shape consisting of many sub-assemblies.
It is for this reason, often, the recognition of a discon-
tinuous object where the parties’ organization is signifi-
The model proposed at the CESR in France [3] to
simulate this part of the image understanding involves 2
phases as follows:
1) Mono or multidimensional cataloging giving a first
meaning to pixels.
2) Isolation and objects extraction using the mathe-
matical morphology laws [4].
The advantage of mathe matical morphology is twofo ld:
its approach is of set (analogy with the photo-interpreta-
tion) and it uses an objective structuring element, defined
by a particular figure of pixels in a given size neighbor-
hood which plays the role of probe affecting identically
all objects regardless of their size and shape by simple
convolution of the image by this stru cturing element.
In image analysis, the models chosen for the extraction
of complex shapes are translated into sequences of mor-
phological operation s.
The organization of the successive phases generally
follows three main conditions: first to place th e phases of
important information loss at the beginning of the se-
quence, then to organize analysis phases according to rea-
sonable logic and finally to identify or reconstruct the in-
formation sought by deletion (or deletion of complement).
Practically, the application of this principle of pattern
recognition usually leads to treat complex noisy images
where th e structur es to char acterize are mixed w ith many
artifacts due to radiometric first processing.
Also, it should be applied to original images in gray-
scale treatment to reduce this effect. Morphological fil-
tering is performed on the channel raw images before
reflectances using classification [5].
3. Application in Case of a Geological Fold
In the example below, the phenomenon sought is a geo-
logical wrinkle of WNW ESE direction. This folds ap-
pears clearly on the image through the shadow effects of
the sun hidden slopes and to the disparity of plant com-
munities and density.
The aim is to extract the structural elements that mate-
rialize it in the Landsat 2 No. 214-30 of November 20,
1980 scene (image obtained by SSM Scanning Radio-
3.1. Filtering and Ranking
The original image consists of both MSS 5 (0.6
m: red) and MSS 7 (0.8
1.1 m: near infrared)
channels. These images (I5 and I7) are filtered by the se-
quences S:
S = A. C. A (1)
where A denotes the morphological grayscale Aperture
and C designates the morphological grayscale Closure.
The morphological Aperture (A) of an image function
is obtained by dilating the function eroded by “f” by the
same structuring element B:
FB (x) = Sup[Inf (f(z))] (2)
With z Bx and y Bx
Morphological closure (C) is:
fB (x) = Inf[Sup(f(z))] (3)
With z Bx and y Bx
The applied filtering provide, thus images S5 and S7:
S5 = A. C. A (I5) (4-a)
S7 = W. C. A (I7) (4-b)
These treatments are chosen to highlight areas of low
reflectance (due to forests and shadows) that marked the
slope indicating the position of the anticline [6]. S5 and
S7 images are then used to produce a classification (Fig-
ure 1) highlighting areas of forest and shade. The so
classified image is then digitalized (Figure 2) by com-
bining these two areas under the X set characterizing the
structure and its complemen tary XC representing the back-
3.2. Isolation of the Set Containing Significant
The binary Aperture of X set obtained by the digital-
izetion of the classified image, followed by a Closure
eliminates small size units and improve the representa-
tion of significant entities.
The Eroded from an X set by a structuring element B,
denoted X
B is defined by the translates intersection of
X by b, b browsing B:
B = Xb (5)
b B
The X dilated by B denoted by X B is obtained by
taking the union of X translates by b:
X B = Xb (6)
b B
The binary Aperture of X by B is the erosion of this
set followed by the dilatation of the result:
XB = (X B) B (7)
The closure is the dilatation of X followed by erosion
of the result:
XB = (X B) B (8)
The binary Aperture and Closure therefore expressed
in terms of intersection and union of translates of the
original set.
Copyright © 2012 SciRes. IJG
Figure 1. Landsat 2 No. 214-30 of November 20, 1980 scene.
Figure 2. Digitalized image.
In our case, an erosion of the binary image, followed
by removal of isolated po ints and a cond itional dilatation
of the result in the original X set eliminates small entities.
We show that this sequence of operations is equivalent to
an Aperture.
The Closure of th e pr eviou s resu lt (Figure 3) regulates
the significant set by clearing holes of small dimension
and in reconnecting very close entities.
The structuring element used for opening and closing
is a Basic Centered Octagon:
At this level, the significant set is formed by entities
representing the regular slopes of the fold and entities
corresponding to other wooded land.
3.3. Extraction of Connected Components
Marking the Overall Structure
The first class of obtained entities is organ ized in narrow
elongated strips marking the sides of the anticline.
The second category may have more complex forms:
in particular, entities of XC complement which can be
included in X, and this comes from the structure of for-
estation on both the slopes of the valleys. The extraction
of connected components marking the structure will op-
erate the criteria of “no holes”, that is realized in three
Figure 3. The closure.
phases schematized by the three following figures.
The “holes” characterization (Figure 4) is followed by
a conditional invasion of th e starting set by these “holes”
(Figure 5).
The connected components (Figure 6) are obtained by
superimposing the result of the invasion with the original
The search for the structure’s overall direction comes
from measurements of intercepts.
One measures the number of points (N) for a set of di-
rections (
The obtained diagram (Figure 7) shows the relation-
ship N = f(
) with 0˚ <
< 180˚ wher e the origin of
the Radiometer Scanning Line.
The diagram showing the number of intercepts as a
function of direction shows a peak that characterizes the
perpendicular direction to that of the structure.
4. Conclusions
This example of aid to geological interpretation of satel-
lite imagery should be considered from the methodolo-
gical point of view. Indeed, the systematic extraction of
an anticline structure should involve other analytical cri-
teria. Such an approach should emphasize the importance
of a set reasoning to exploit remote sensing images. This
often leads to propose relevant observation criteria where
numerical modeling overcomes the variability of visual
interpretation. In this framework, mathematical morpho-
logy is generally a source of possible sequences for nec-
essary treatments.
The proposed approach as an aid to automatic image
understanding is not intended to provide automatic
methods that can still detect the same phenomenon. By
cons, this approach shows the types of procedures
adapted to the phases of reasoning that cannot be consid-
ered to model the whole.
Finally, it is clear that the process of set-photo-analytic
interpretation is often well represented by the methods of
mathematical morphology. It is of course possible to ap-
ply this sequence of processing to other topics such as
major plant and soil areas. However, such an assertion is
Copyright © 2012 SciRes. IJG
Copyright © 2012 SciRes. IJG
Figure 7. Number of intercepts related to direction.
Figure 4. Holes characterization.
often made difficult by the complexity and diversity of
the natural environment.
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Figure 6. Connected components.