1. University of Genoa
Classification of Remote Sensing Images of Large Urban
Zones using Mathematical Morphology
Eridon Brahimllari
Supervisor: Co-supervisor:
Gabriele Moser Vladimir Krylov
Academic Year 2014-2015
Master Thesis

2. Outline
• Remote Sensing and its Applications
• Land cover classification in urban areas
• Mathematical Morphology
• Differential Morphological Profiles
• Support Vector Machines
• Data sets for the experiments
• Experimental results
• Conclusion

3. Remote Sensing and its Applications
• RS is the science of obtaining information about objects or areas using measurements
acquired from a distance without contact with the investigated objects or areas
• In the case of Earth observation, sensors are usually onboard satellite systems or
aerial platforms.
• RS provides important technologies to support environmental applications such as
1. Urban planning
2. Land-use and land cover mapping
3. Risk assessment and mitigation
• High resolution (HR) optical images play an important role in applications to urban
planning because of the spatial information and geometrical detail they provide.

4. Land cover classification in urban areas
• High resolution image classification provides a collection of methodological tools to
support urban planning.
• Land cover mapping in urban areas is a challenging classification problem due to the
need to define accurate models for the spatial-geometrical information associated
with the imaged urban areas.
• Objective of the thesis: exploring experimentally the approach based on Differential
Morphological Profiles (DMP) to feature extraction, within the application to the
classification of large urban zones.

5. Mathematical Morphology
• An important family of techniques, based on set theory, for the processing of images including
geometrical structures
• Erosion and dilation, with respect to a predefined structuring elements (SE), are the basic
operators leading to several morphological transformations such as
1. Reconstruction by dilation, 𝜌 𝐼
𝐽 = ∨ 𝑛>1 𝛿 𝑛
𝐼
𝐽 𝑠𝑢𝑐ℎ 𝑡ℎ𝑎𝑡 𝛿(𝑛)
𝐼
=𝛿(𝑛+1)
𝐼
2. Reconstruction by erosion, 𝜌∗ 𝐼
𝐽 = ∧ 𝑛>1 𝜀 𝑛
𝐼
𝐽 𝑠𝑢𝑐ℎ 𝑡ℎ𝑎𝑡 𝜀(𝑛)
𝐼
= 𝜀(𝑛+1)
𝐼
• The usefulness of such operators is strictly related to the extraction of spatial and geometrical
information from the input image.

6. Differential Morphological Profile
• DMP is a feature extraction method constructed by using morphological profiles (MPs) and applied
to urban area images in order to model spatial information related to geometrical objects.
△ 𝛾 𝑥 = Δ𝛾 𝜆: Δ𝛾 𝜆 = Π 𝛾 𝜆
− Π 𝛾 𝜆−1
, ∀ 𝜆 𝜖 1, ⋯ , 𝑛
• The MP is composed of results of opening and closing by reconstruction obtained using various
sizes of the SE.
• Opening by reconstruction is obtained from erosion and reconstruction by dilation which have the
property to remove objects smaller than the SE.
• By iteratively varying the size of the SE, it is possible to extract multiscale spatial features that are
useful to the classification of urban areas.

7. Support Vector Machines
• A Support Vector Machine (SVM) is used to classify in the feature space of the spectral
channels of the input image and of the extracted DMPs.
• The main ideas of SVM are to separate feature vectors with a linear decision boundary in a
nonlinearly transformed space, and to maximize the margin between the different classes.
Maximize 𝐿 𝐷= 𝑖 𝛼𝑖 -
1
2 𝑖,𝑗 𝛼𝑖 𝛼𝑗 𝑦𝑖 𝑦𝑗 𝐾(𝑥𝑖, 𝑥𝑗)
subject to 𝑖 𝛼𝑖 𝑦𝑖= 0 , 0 ≤ 𝛼𝑖 ≤ 𝐶 ∀𝑖
𝑓 𝑥 = 𝑖 𝛼𝑖 𝑦𝑖 𝐾(𝑥, 𝑥𝑗) − 𝑏
• SVM is applicable to overlapping and non-linearly separable classes by using kernel functions.
• It has been demonstrated effective in dealing with high dimensional data with little need for
possible feature reduction.

8. Data sets for the experiments
Training map (Osnabruck)
Aerial RGB image acquired over Osnabruck, Germany,
1-m spatial resolution
Buildings
Trees
Low vegetation
Vegetated arable
land
Roads & Pavements
Bare soil
Land cover Types.
Buildings
Trees
Low vegetation
Vegetated arable
land
Roads & Pavements
Bare soil
Water
Land cover Types
Training map (Amiens)
Satellite NIR-R-G image acquired by SPOT5
over Amiens, France, 5-m spatial resolution

9. Experimental Results
Case Study of Amiens
• OA - Overall Accuracy
• AA - Average Accuracy
• PA - Producer Accuracy
Test
Accuracies
With
DMP
Without
DMP
PA -Buildings 68 % 63 %
PA -Bare Soil 73 % 73 %
PA -Roads &
Pavements
52 % 54 %
PA - Low
Vegetation
63 % 61 %
PA - Trees 94 % 94 %
PA - Arable
land
95 % 91 %
PA - Water 78 % 78 %
OA 80 % 79 %
AA 74 % 73 %
Quantitative accuracy analysis
using test samples
Buildings
Trees
Low vegetation
Vegetated arable
land
Roads & Pavements
Bare soil
Water
Land cover Types.
A region from the original image, Amiens.
Classification map using DMP.
Classification map without using DMP.

10. Experimental Results
Case Study of Amiens
Classification map using DMP
Classification map without using DMP
A region from the original image acquired over Amiens.
• Visually quite similar classification maps
• However, improvements in areas including
geometrical structures can be noted.

11. Experimental Results
Case Study of Osnabruck
Classification maps for a given region of the original image. (a) original image,
(b) classification map without using DMP and (c) classification map using DMP.
(a) (b) (c)
Buildings
Trees
Low vegetation
Vegetated arable land
Roads & Pavements
Bare soil
Land cover Types
• Better visual discrimination of the classes
that exhibit geometrical structures.
• In the classification map obtained using
DMP:
1. Buildings are more accurately detected.
2. Roads & Pavements are more correctly
discriminated.

12. Experimental Results
Case Study of Osnabruck
Test
Accuracies
With
DMP
Without
DMP
PA - Buildings 90 % 89 %
PA - Bare Soil 95 % 53 %
PA - Roads &
Pavements
89 % 84 %
PA - Low
Vegetation
75 % 82 %
PA - Trees 77 % 98 %
PA - Arable
land
68 % 76 %
OA 80 % 83 %
AA 82 % 80 %
• OA - Overall Accuracy
• AA - Average Accuracy
• PA - Producer Accuracy
An imaged area describing mainly a field of bare soil.
The related classification map exhibits artefacts.
Quantitative accuracy analysis
using test samples.

13. Conclusion
• Quite high accuracies on challenging high-resolution data sets suggest the possible effectiveness of
DMP as a feature extraction tool for urban applications.
• The qualitative visual analysis of the resulting classification maps shows a better discrimination of
classes with geometrical structures when DMP features are used.
• The DMP approach also suffers from some limitations:
1. DMP is less useful to the discrimination of classes characterized by non-geometrical textures.
2. Computational complexity increases linearly with the number of multiscale levels in the profile.
3. Artefacts could arise due to the moving window approach taken by DMP.
• In order to overcome these limitations, future work might consider the application of the so-called
Attribute Profile (AP) features.

14. References
• M. Pesaresi and J. A. Benediktsson, “A new approach for the morphological segmentation of
high-resolution satellite imagery,” IEEE Trans.Geosci. Remote Sensing, vol. 39, pp. 309–320
Feb. 2001.
• Gabriele Moser, Sebastiano B. Serpico, and Jo´n Atli Benediktsson,“Land-Cover Mapping by
Markov Modeling of Spatial–Contextual Information in Very-High-Resolution Remote Sensing
Images”, Proceedings of the IEEE vol 101,No.3,March 2013.
• P. Soille, Morphological Image Analysis: Principles and Applications.
Berlin, Germany: Springer-Verlag, 1999.
• Vapnik V, “The nature of statistical learning theory”, Springer-Verlag, 1995.
• John A. Richards and Xiuping Jia,“Remote Sensing Digital Image Analysis” ,4th Edition,
Springer,2006.