## 4 Methods
### Mapping Artificial Areas from OBI4
#### 4.1.1 Procedure for Classification of RapidEye Images Acquired in 2009  
To map artificial areas, we developed an OBIA procedure comprised of two successive steps of segmentation-classification (Figure 3). The first step allows the area to be stratified into regions of the same nature. The second step consists of reclassifying each of these regions to extract artificial objects. This method allows the parameters and thresholds of each class to be adapted to the local context.  
Next we provide a short description of the procedure. The segmentation parameters and feature classes for each step are summarized in Table 2.  
Step I, \"classification of coarse land cover\" allows the landscape to be stratified into six land cover categories: C1-\"agriculture areas\", C2-\"coastal wetlands\", C3-\"forest areas\", C4-\"roads\", C5-\"water surfaces\", C6-\"urban areas\". To this end, a coarse segmentation level is first created by implementing the multi-resolution segmentation algorithm using information from both thematic (roads) and raster (all bands of RapidEye) layers. In this way, the study zone is segmented according to the limits of both thematic data to product coarse land cover objects. Second, each object is assigned to a land cover category using fuzzy classification rules based on thematic attributes for the C4 category, and raster attributes (spectral and context) for the other categories (see Table 2). For example, the normalized difference vegetation index (NDVI) was used to distinguish artificial and agriculture areas. Finally,neighboring objects in the same category are merged into a single object using the merge algorithm included in the software.  
Step II, \"extraction of artificial area\" allows all artificial objects to be extracted and assigned to one of the three categories extracted in Step I: C1-\"agricultural areas\", C3-\"forest areas\" and C6-\"urban areas\". To this end, a finer segmentation level based on image object limits in step 1 and RapidEye band information is first obtained by using the multi-resolution segmentation algorithm. This segmentation level is needed for satisfactory delimitation of all artificial objects including small isolated objects. Then, each object at this level inherits the land cover categories from step I using class-related features at the super-object level. An \"artificial areas\" class is allocated to all objects in the C4 category. For each object classified in C1, C3 and C6, category specific classification rules based on spectral and textural attributes (see Table 2), are used to distinguish the \"artificial areas\" class. For instance for objects that belong to the C3 category, a classification rule based on near infrared information is used to separate \"vegetation objects\" from \"building objects\", the latter being extremely small and isolated in this region [28]. Finally, manual classification processing is performed to reduce confusion between artificial objects and agricultural or semi-natural bare soil objects that have very similar spectral and textural behaviors.  
Figure 3: OBIA procedure for mapping artificial areas from 2009 RapidEye images. This procedure is composed of two successive segmentation-classification steps.