Download Mohamed Sellami (1), Ferdaous Chaabane (1), Catalin Fetita (2)

MORPHOLOGICAL SEGMENTATION OF MULTISPECTRAL IMAGES FOR LAND
COVER MAPPING
Mohamed Sellami (1), Ferdaous Chaabane (1), Catalin Fetita (2)
URISA, Ecole Supérieure des Communications de Tunis (SUP’COM), Tunisie.
ARTEMIS, Institut National des Télécommunications (INT-EVRY), France.
1. INTRODUCTION
The availability of satellite images as well as their high resolution are a support for the development of new remotely-sensed
techniques such as 3D reconstruction, indexing, classification and segmentation. In the case of classification and
segmentation for example, the results allow the photo interpreter to read directly and automatically the distribution of the
different land cover classes.
There is unfortunately no general solution to the problem of segmentation, but rather a set of mathematical tools and
algorithms that can be combined together to solve specific problems. The mathematical morphology offers a theory and
operators that can be locally applied to images in order to extract semantic information. This theory has found wide
applications in medical imaging, material science and machine vision but it has sporadic applications in processing of remote
sensing data [1] in particular of multispectral data.
The purpose of this paper is to study the viability of morphological operators to segment remote sensing data. Thus, we
propose a new segmentation method using mathematical morphology tools for land cover classification applied to four bands
SPOT4 multispectral data. This approach combines contextual information from image segmentation and spectral
information which characterize better the ground covering than panchromatic information.
The paper is organized as follows. First, the morphological segmentation method is presented in section 2 by considering
each region of interest separately. We take into account four regions which are water, urban, vegetation and uncovered areas.
Secondly, section 3 exposes and comments the obtained results for each land cover class.
2. MORPHOLOGICAL SEGMENTATION
Previous works usually exploit watershed-based segmentation to discriminate complex ground covers components found in
satellite images [2] [3]. The principal difficulty of this technique is the oversegmentation which is generally reduced by
introducing other processing tools.
In this paper, we proceed differently, the proposed morphological methodology extract each region of interest by taking
advantage from the nature of the area and its reflectivity in different spectral bands (I1, I2, I3 and I4). As we consider four
different areas, the segmentation is performed in four steps, each of them using one or several appropriate spectral bands. In
the following, each segmentation step related to each area is presented separately.
2.1. Water areas
The water areas are characterized by a dark appearance due to the spectral features of band 4. Four steps are followed to
extract these areas: We start by thresholding the original image I4. This first step allows us to extract both water and other
areas. As result, we have a binary image that will be used as a mask for the next reconstruction phase. Second, we apply a
geodesic reconstruction by erosion of the original image over the resulted binary image. Thanks to this reconstruction, we are
able to smooth out the areas with a high gray level without modifying the nature of marked water areas. Third, we apply a
second geodesic reconstruction by erosion of the precedent reconstructed image over the same image increased by the first
threshold. This third step is done because the reflectance coefficients of the second step are quite close; we can not
discriminate between water areas and uncovered ground by simple thresholding. Indeed, by processing this second
reconstruction, we enhance the gray level of the water areas and maintain the same color level of other areas. Finally, we
compare the gray level of the two reconstructed images in order to extract water areas.
2.2. Uncovered ground areas
To extract uncovered ground, we use the second, third and fourth spectral bands images (I2, I3 and I4). First, we apply a
contrast enhancement by a linear combination between the two bands images I3 and I4. Second, since the enhanced image
shows some plots of vegetation, we remove them by subtracting the difference image (I3 - I2) from the enhanced image. The
image (I3 - I2) characterizes the vegetation index known as NDVI (The Normalized Difference Vegetation Index). At the
third step, some parts of roads remain. Thus, we apply a alternating sequential filter (opening and closing) in order to smooth
small components without changing the shape of the region of interest. Finally, we get the final uncovered ground mask by
subtracting the mask of water areas from the filtered image.
2.3. Vegetation areas
As mentioned above the difference between the two spectral bands 2 and 3 gives an enhanced image (I3 - I2) which has been
chosen according to the NDVI. This image allows extracting vegetation areas in three phases: First, an alternating sequential
filter is applied to smooth out the third spectral band image. This kind of filter preserves the vegetation structure and
smoothes the urbain areas. Second, we apply a geodesic reconstruction by a dilatation of the filtered image over the raised
image. The resulting image contains both vegetation and uncovered ground areas. This result is expected. Indeed, as we have
deployed the NDVI to mark the image, the uncovered ground areas as well as the vegetation areas are taken into account.
Finally, we subtract the uncovered ground mask from the reconstructed image.
2.4. Buildings and urban areas
The water as well as the uncovered ground areas have equivalent responses in spectral bands 1 and 3. However, buildings
and vegetation areas have complementary responses. For this reason, the contrasted image appears as a good support to
extract urban areas. The buildings areas segmentation is done in three main phases: First, a binary mask, including the main
buildings contained in the enhanced image (I1 – I3) should be determined. For that purpose, we apply the h-maxima operator
followed by an alternating sequential filter in order to extract local peaks. Second, a geodesic reconstruction by dilatation of
the filtered image is applied. The reconstructed image includes only buildings areas and some water areas. Third, a
binarisation with an appropriate threshold is applied to discriminate water areas.
3. RESULTS
The experiment was conducted using four bands SPOT4 multispectral image. The study area is the city of Tunis, Tunisia.
The SPOT4 image was segmented using the technique described above in order to extract water, uncovered, vegetation and
urban areas. The promising segmentation results for each region of interest are shown in figure 1.
A
B
C
D
E
Figure 1. Morphological segmentation results: A) SPOT4 Image (Spectral band 1); B) Water areas mask C) Uncovered ground areas mask; D) Vegetation
areas mask; E) Urban areas mask.
4. REFERENCES
[1] P. Soille and M. Pesaresi, “Advances in mathematical morphology applied to geoscience and remote sensing,” IEEE Transactions on
Geoscience and Remote Sensing, vol. 40, no. 9, pp. 2042-2055. 2002.
[2] P. Li and X. Xiao, “Evaluation of multiscale morphological segmentation of multispectral imagery for land cover classification,” in
Proceedings of IEEE International Geoscience and Remote Sensing Symposium, vol. 4, pp. 2676-2679. 2004.
[3] A. Plaza et J. C. Tilton, “Automated selection of results in hierarchical segmentations of remotely sensed hyperspectral images,” in
Proceedings of IEEE International Geoscience and Remote Sensing Symposium, vol. 7, pp. 4946-4949. 2005.