2"dGRSSLSPRS Joint Workshop on "Data Fusion and Remote Sensing over Urhan Areas"
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AUTOMATIC CLASSIFICATION OF LAND COVER WITH HIGH RESOLUTION DATA OF THE
RIO DE JANEIRO CITY BRAZlL Luiz Felipe Guanaes Rego Barbara Koch Abteilung Femerkundung und Landschaftsinfonnationssysteme - FELIS Albert-Ludwigs-Universitat Freiburg Forstwissenschafiliche F a h l t a t Tennenbacher Strasse 4 * D-79106 Freibur Tel.:(49)-761-203 3694 * Fax:(49)-761-203 3701
[email protected] Abstract The city 01Rio de Janeiro, with more than 5 million inhabitants, have two big mountains in its centre with natural Atlantic rain forest. The growth of the city around these two mountains puts pressure on this lorest. This not only the endangers the remaining Atlantic forest (one 01 the most endangered lorest types of the world) but also causes landslides and mud flows. The city administration carried out a Landsover forest classification with visual interpretation using merged LANDSAT and SPOT data. This work produced a compatible thematic map in the scale 1:50,000. It took about 36 mouths 01 intensive work and high costs to produce these maps. The scale of these maps permit to have a global vision of the land change cover but unfortunately do not correspond with the geographic information system of the city, which works with a scale of 1:10,000. The city searched for options to make this work automatically and quickly to get information for planning and to propose solutions. In order to solve this problem high resolution satellite data and automatic classification of Land-cover classes are needed. Consequently, images as IKONOS (multispectral with four bands and 4 meters) need to be used to produce a classification, with a scale corresponding to the CIS of the city. The automatie classification of Laud-cover classes will provide a relatively rapid classification. Pixel based elassilication with high resolution data show some problems because the level 01 information in the data produce a lot of incorrect classified pixels. The solution t o perform this classification uses the new approach that makes one "pre-classification" (segmentation), which transforms the pixel information in objects as well as the feature in the vector representation. This objects have spectral value (for example the medium value of the pixel inside of them), information about the area, the length, the shape etc. To carry out the segmentation and classification processes, oriented objects analysis are used
overall accuracy. The hypothesis that with high resolution data automatically produce Land-cover classification, corresponding to the scale 1:10,000, is true.
we can
I. INTRODUCTION Rio de Janeiro City is the country's second biggest city and has a 5,848,914 inhabitant population (Statistics Yearbook, RJ city, 2000). The city presents contradictions that express themselves spatially through two cities: a city with welldeveloped infrastructure and services, and a city with no infrastructure, with sanitation, electricity, power and service problems. These two cities in one experience a negative shocking process that leads to wrong-doings and environmental degradation processes. This duality may be seen spatially, for instance, between the noble city districts situated in the south of the city and the slums (they are illegal occupations, or occupations that are going through the process of being legalized, usually on hills, where people settle. The areas are inappropriate and there is the constant risk of sliding.). This spatial contradiction brings an articulation between service renderers and service absorbers, using a logic in which one depends on the other and, somehow, they justify themselves (Cor&, Roberto Lohato, 1997).
These factors show the complexity of the spatial occupation of the city. They prove the need to produce Land-cover and Land-use maps that may enable the perception and study of
(ecoGnifion).
spatial anangements that these pressures materialize.
To achieve these results a method that operates in 3 phases was applied. Firstly, three generic classes were created, basically using spectral inlormation. Secondly, six subclasses with their respective neighbourhood information were applied. Thirdly, 4 more classes were created irrespective of Classification above. These classes involve one hierarchy 01 segmentation with objeets 01different sizes. The classification process used one level with small classified objects to make a classification of objects in another level containing bigger objects. In all phases were created reference points to make the accuracy assessment. The results in all were more than 85%
Land-cover maps were developed through visual interpretation techniques, which demands intensive analytical work and several months for its performance (Prieto, D. F., et al., 2002). Many times this time between the acquisition of the image and its classification does not let us accompany the process and there is no time to substantiate the making of a decision avoiding or diminishing the negative effects of urban expansion concerning the municipality's forested areas.
2ndGRSSlISPRS Joint Workshop on "Data Fusion and Remote Sensing over Urban Areas" The Land-cover classifications of the city were developed using medium-resolution satellite images, LANDSAT and SPOT, which make way for the production of classifications based on the spatial resolution of these images that lead to maps that are compatible with the scale of 1:50,000 (Technical Report. SMAC. 2000). On the one hand, this scale does not help with the perception of the devastation processes that take place in many areas and involve few meters of the expansion, but that when added represent considerable areas. On the other hand, too, they do not allow the integration of this information to the GIS environment developed by the city that
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irregular urban expansion, agricultural a~easwithin the park's limits and constant problems caused by annual tires. The coverage classes used by City Hall that belong to the area of study of this work may be grouped according to their hierarchy, that is, generic classes may be established and then subdivided into subclasses. Most of the Land-cover classification systems adopt this process of establishing classifications at different levels of detail (Meinel, G. & Hennersdorf, J., 2002). The process to evaluate classification techniques used in
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Fiaure 1. Characteristics of the Phases of the Research Proiect
works with information that is el'ther compatible or higher than the scale of 1:10,000, leading to a mistake, by both factors, between the environment of geographic analysis and the coverage information that is produced. In short, it is necessary to search for a classification process that in weeks enables the production of Land-cover maps of the city. It is also necessary that these maps have a level of detail that enables the identification of the fragmented process of urban expansion concerning the forests. The proposed, therefore, involves the verification of the capacity to reproduce automatically and, consequently, quickly, the city's official coverage classes using high-resolution IKONOS images that lead to classifications that are compatible with the scale of 1:10,000. Therefore, there was an attempt to compensate the medium-resolution and the visual interpretation versus the high-resolution and the automatic classification.
this work made use of this hierarchic concept of classes, dividing the problem into three different levels of detail called phases. With the phases, the results of the automatic classification techniques were tested from general to specific, beginning with three generic classes and ending with all the Land-cover subclasses used by City Hall. The first phase evaluated a set of three generic classes, in a relatively small sub-set using a great deal of classification techniques. Phase 2 evaluated a set of six classes, in a sub-set four times larger than the first one and with a lot less classification techniques. Phase three evaluated a set of 12 classes, in a sub-set that represented half of the IKONOS scene and applied a classification technique. Each phase was distinguished by a set of factors: the classes that were used, the sub-set's area and the number of options for classification. These factors varied from phase to phase: the classes and the sub-set's area increased and the number of options for classification decreased as it may be seen in Figure 1.
11. METHOKILOGY
This work was concentrated on part of the Pedra Branca State Park, in the city of Rio de Janeiro, Brazil. The Park was chosen due to the huge environmental problems the park faces, especially because of its insertion within the city of Rio de Janeiro. It was also chosen due to boundary limits such as:
With the increase in the number of classes, from generic categories in the first phase, subclasses were created, deepening what is described on the landscape printed in the IKONOS image. The final product obtained in the third phase was 12 classes that were merged in 5 classes used by City Hall.
Znd GRSSDSPRS Joint Workshop on "Data Fusion and Remote Sensing over Urban Areas"
The increase of the number of classes from phase to phase coincided with the increase of the subsets' area that were evaluated. With this alteration, there was an attempt to confirm, on the one hand, the stability of the classification results, as, on the other hand, to increase the number of objects in the image that represented the classes intended to extract in each phase. We also tried to give a more realist touch to this work, looking for a practical solution instead of more theoretical results that may be found, for example, in a fixed sub-set that could not embody. the whole variance spectrum that holds the landscape represented in the complete IKONOS image.
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Phase 1 tested too the Membership classifier that proved to be, despite the large amount of available classifiers, inferior to the Nearest Neighbor classifier. The use of the Nearest Neighbor suggests that generic classes, which involve objects with great variations within one only class, reach the best results with the Nearest Neighbor as it may be seen in Graph 2. Nearest Neighbor x Membership
6 0,895 0.89 - 0,885 E 0.88 0,875 0,87
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Since in each phase a different group of classes in subsets with different areas too was tested, it was necessary to develop three references, one per phase, to verify the accuracy of the classifications.
In short, one phase was defined as a set of experiments with a fixed geographic area, the same set of classes and with the same accuracy system. An experiment was defined as a unit of study and evaluation that embodied the study of one or two variables with different values and tried to answer an objective question related to a technique and a result that could be compared to another technique's result.
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GRAPH 2. THEBESTCLASSlFlCAllON RESULTS FROM E(p 102 (NEARFSTNEIOHBOR) ANDW le3 (MEMBERSHIP).
Phase 2 involved six classes that were extracted in two steps. The first involved the three classes from Phase 1: Vegetation, Field and Edification. The second one, six classes: Grouped Vegetation, Urban Vegetation, Urban Field, Grouped Field, Isolated Edification and Grouped Edification
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111. DISCUSSION AND RESULTS
Phase 1 tested three generic classes, Field, Vegetation and Edification, and a great number of automatic classification techniques of orbital images. These techniques involved classifications based on the pixel as well as classification techniques based on objects. The results concerning their accuracy showed in more than 100 classifications that the system based on objects had a better classification than the one based on the pixel using high-resolution images as it may be seen in Graph 1.
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Graph I. The 40 best results from experiments A (pixel based) and the 40 best results from experiments B (object based)
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Figure 2. Classes from Phase 1 and 2.
from relations of neighborhood between classified objects. These classes were developed within the consistency principle used in the Land-cover classifications. The second group is composed of subclasses of the generic classes extracted from the first group, Phase 1, as we may see in Figure 2 Phase 2 confirm potential to describe eminently contextual classes in the object environment. On the other hand, contextual relations proved to be very broad and flexible, in this aspect, the logic for the creation of these classes and the definition of their contextual relations used by the analyst are particular and different solutions may lead to effective results.
2"' GRSSLSPRS Joint Workshop on "Data Fusion and Remote Sensing over Urban Areas"
Phase 3 involved classifications that are direct answers to the previous phases. More than this, the previous phases exist within Phase 3. This way, level 2 reproduced the three generic classes from Phase 1 with the Nearest Neighbor classifier. Level 6 reproduced the six classes developed in Phase 2 using contextual neighborhood classifiers and, finally, levels 17 and 9 created the defined classes using sub-objects classified as Vegetation 6 and Urban Field 6. The results from levels 6, 9 and 17 are integrated at level 7 producing a group of eight classes that besides considering the classes defined by City Hall, created other classes that prove the potential of classification of high-resolution images as well as of the classifying potential of the object environment. The classes from Phase 3 were produced using the classes developed in Phase 2. The Urban Vegetation, Urban Field and Grouped Edification classes were added to the Urban Area class that, later, was subdivided into the Urban not Consolidated and Urban classes. The Vegetation class was subdivided into Vegetation and Altered Vegetation and the Grouped Field class was renamed to Field as we may see in Figure 3.
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Field 6 class at level 6 with the following classes: Urban 91 and Urban not consolidated 91. The classes from level I were developed based on proportion relations of the sub-objects classified at level 6 and of the super objects classified at levels 9 and 17 with the following classes: Grouped Vegetation 7111, Altered Vegetation 7, Field with Vegetation 711, Field with Isoluted Vegetation 711, Grouped Field 7, Isolated Edification 7, Urban 71 and Urban not Consolidated 71. Figure 4 shows the segmentation levels, the classes and the relations among these levels in terms of sub-objects, blue arrow, and super objects, red arrow, used in Phase 3. Level 7 corresponds to the Phase's final result.
Phase 3 took advantage of the inherent potential of classes and subclasses within a more complex environment involving relations among objects and sub-objects, leading a subclass like the Urban Field to work as a basis for the classification of super-objects in two classes: Urban and Non-Edijiid
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1 among w his level of relations levels proved to be
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The production of classes in Phase 3 involved a classifying process among different levels, with generic planes (high scale) where large objects were classified using classes found in a lower plane (low scale) (ecoGnition Manual: Basic Concepts). So we created at level 6 a group of classes that worked a base to classify other classes at level 9 and level 17. These classes were composed of large objects and created other classes at level 7 that included the classes used by City Hall in the area covered by the sub-set of the image used in the Phase. The classes from level 17 were developed based on proportion relations of the subobjects classified as Field 6 and Vegetation 6 classes at level 6 with the following classes: Vegetation 17, Area of Altered Vegetation 17, Area of Field with Vegetation 17, Field 17 and Urban 17. The classes from level 9 were developed based on proportion relations of the sub-objects classified as Urban
powerful but once again too dependent on the analyst's positions. This participation is not limited to the definition of samples and evaluation using statistical parameters, TT.t.nl -,.,r n n r n i ; ~ obut t n to ~ the development of a Urban spatial logic that enables the classes intended to classify to 1 be developed in sequential-and interrelated phases.
Therefore, the classification process within an object environment requires the creation of logical hierarchies to reach the intended class within a context of relations, of classes and subclasses as well as using relations among objects from one class to another. This system resembles the one used for visual classification in which intelpreters select big groups and, using varied parameters, they subdivide these groups into sub-groups and so on, from the generic to the specific. The integrated results of the three phases of this project lead us to assert that the use of IKONOS images and the object environment enable the reproduction of classes used as coverage by the Rio de Janeiro City Hall. This classification may be developed automatically and may lead to results compatible with the scale of 1:10,000 and with higher accuracy results compared to the current classification developed by City Hall, 87% and 61%, respectively as it may be seen in Graph 3.
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REFERENCE
C o d a , Roberto Lobatu,L997.Trajetbnas Geogrlficas - Meio Ambiente e Sociedade. Berlrand. B r a d [2] Pieto, D. F. at all, 2002. Development of EO derived Products and Service for Moniwnng Urban Sprawl at National Scale: URBEX, a joint ESA-WWF Effort. Proc 3 international Symposium Remote Sensing of U h Areas Stambul: Page 497503. [3] Technical Report. SMAC. 2000. Mapeamento e caracterizaglo de "SOS das term e cokbermra vegetal no municipio d o Rio de Janeiro entre os anos de 1984 e 1999. Secretaria Municipal de Meio Ambiente. Rio de Janeiro. 141 Meiael G,& Hennersdorf, I., 2002. Classifications Systems of Land Cover and Land Use And their Challenges for picture Processing of Remote Sensing - Slatus of Intemational . In Proceedine 3 intemacional Symrasium Remote Sensing of Urban Areas. Stambd.
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Automatic classification
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Graph 3. Automatically classification (IKONOS) compared to
visual classification the current classification developed by City Hall (LANDSAT-SPOT).
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Conclusion
This work confirms the hypothesis first defined and suggests City Hall to adopt high-resolution images to produce Landcover maps, with high accuracy levels, more details on the classes and produced in few months, opposed to the mapping produced from LANDSAT images, assuring updated information on the dynamics of transformation of the municipality's space Urban not :onsolidated 91 and, consequently, establishing management and planning actions of the city. Despite the specificity of the area of study and of the classes used, this work enables us to generically substantiate the of the potential classification system via objects as well as the IKONOS images to use in Land-cover classifications, especially in urban areas due to the complexity of the classes in this environment as well as for the need of mappings on great scales aiming at actions at management and operational levels.
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Figure 4. Classification process of Phase 3. The arrows in red indicate classes classified from super objects relations and the arrows in blue, from sub-objects relations.
