The segmentation

eCognition has been extensively used and the principles of segmentation have been rehearsed in a number of works, for example Benz et al. (2004), Benz & Schreier (2001), Blaschke (2005),

Laliberte et al. (2004), Willhauck (2000) and Zhong et al. (2005). These authors have explained the concepts that are needed to understand the processes for classifying land cover. Their explanations and those of others will be recounted below.

We humans think and make perceptions as humans. We are not computers and do not programme what we see in the world by numbers. Therefore, is it not better that when software analyses an image, it is seen as humans do? When humans see things, they are performing a complex mental procedure known as image understanding (Definiens Imaging GmbH 2008). Our eyes automatically look at an area and register certain areas according to their shape, colour and size. Thus, the things we see become objects.

When looking at Figure 2.1, we see that the area has a green background with three different shapes inside. Our brains register each one of these shapes individually and they are sorted as three different objects. Each object has a relationship to the scene, a relationship to other objects and characteristics about itself. Similar to human vision, the Definiens concept of image understanding is based on a correct segmentation of the visual image content of interest against other visual image content. Segmentation is performed by splitting the image into zoned partial areas of differing characteristics. The segments are called image objects. Segmentation is defined as the subdivision of an image into separated regions or objects on the grounds of some form of similarity of reflection values and shape of polygons (Definiens Imaging GmbH 2004).

Figure 2.1 Image with different objects

eCognition‟s multiresolution segmentation is a bottom-up region-merging technique starting with one-pixel objects (Benz et al. 2004; Zhong et al. 2005). This means that the process starts with a single pixel and through a series of calculative steps, pixels are grouped together until a threshold is

met. Parameters set at the beginning of the segmentation exercise determine the thresholds reached. Segmentation parameters are based on three determinants: scale, colour (spectral information) and shape (smoothness and compactness) (Laliberte et al. 2004). The colour parameter balances the colour homogeneity of a segment on one hand and the homogeneity of shape on the other (Willhauck 2000). Colour and shape can be weighted from zero to one while scale is a unitless parameter related to image resolution (Laliberte et al. 2004). The segmentation process can be run at various scales and each scale lies in a separate level, meaning that pixels can be grouped into smaller objects at the first level and move up to larger objects with more pixels grouped together. Repeated segmentations with different parameters create a hierarchical network of sensible image objects and each object knows its relationships to its neighbour objects and sub- and super-objects (Willhauck 2000). Figure 2.2 shows how pixels with similar characteristics are grouped together into the same object. For example, for the vineyards (red areas) to be extracted entirely, shape and colour parameters are emphasised. The brightness index which assigns a value to an object based on its brightness, and normalised differentiation index (NDVI) are good determinants for segmenting the lighter yellow areas of low vegetation growth.

Figure 2.2 Segmentation showing clearly defined objects according to spectral characteristics

A complete classification task consists of subtasks, which have to operate on objects of different sizes (Benz et al. 2004). The segmentation at different layers works on a hierarchical structure

where each object knows its neighbouring object as well as its sub-objects (those objects in a level below) and its super-objects (those objects in a level above). Zhong et al. (2005) give a good description of hierarchical image segmentation. They point out that segmentation at a coarser level of detail can be produced from simple merges of regions from segmentations at finer levels of detail. Figure 2.3 displays a simplistic description of how a super-object is reduced to smaller sub- objects in each level. Each level is created below the one preceding it. Thus the last level segmented at the smallest level is Level 4. Following Level 4, larger objects are created at Level 3 and so on. Another factor Blaschke et al. (2000) point out is that image data exhibits characteristic textural information which is often neglected in common classifications. The texture of an image can be determined according to its smoothness or its coarseness (Blaschke et al. 2000).

Figure 2.3 Pictorial description of image object levels

What has been described above was explained graphically with the aid of the satellite image in Figure 2.4, which shows the result of a multi-resolution segmentation process. A threshold is applied that factors the size that the objects will be. A threshold is a numerical value assigned to the segmentation. Figure 2.4 clearly illustrates how the objects increase in size as the threshold increases. Colour, shape and texture are assigned weights commensurate with their importance. The

higher the weight value the more important the criterion. If the weight value of colour and shape is high and that of texture low, eCognition will weight the former two with more importance.

Source: Definiens Imaging GmbH (2008: 27) Figure 2.4 Image object levels with differing thresholds

The segmentation process is the basis of the object-oriented principle and is therefore the most important step. Following the segmentation phase, the classification of the image objects can begin. All subsequent analyses of the imagery will be based on the image objects generated because each object has a relationship to the scene, its neighbour, super- and sub-objects as well as having its own characteristics and numerical values. The concepts of and literature on object-oriented classification systems are discussed in the next section.