Segmentation-based land use classification: Object-oriented image analysis Segmentation means the grouping of neighboring pixels into regions (or segments) based

on similarity criteria (digital numbers, texture). Image objects in remotely sensed imagery are often homogenous and can be delineated by segmentation. In remote sensing, the process of image segmentation is defined as: “....the search for homogenous regions in an image and later the classification of these regions” (Mather, 1999). It can also be regarded as object-oriented image analysis. The concept of object-based analysis as an alternative to pixel-based analysis emerged as early as the 1970s (de Kok et al., 1999). It is based on always the basic processing units are objects (segments). Different approaches exist, but one approach, implemented in the software package eCognition (Baatz and Schape, 2000), is a so called a multi-resolution segmentation procedure.

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Subset

Mosaic Stacking

Subset core area

Figure 20: Step by step procedures of Landsat image processing: image stacking, mosaicing and subsetting the required area.

The image objects are built in the first step of the classification. Different colors are applied for different classes (Meng et al., 2009). Subsequently, bigger segments are created by merging pairs of image objects using homogeneity criteria (Rahman and Saha, 2008). A homogeneity criterion is defined as a combination of color homogeneity (i.e. standard deviation of the spectral color) and shape homogeneity (i.e. compactness and smoothness of the shape) (Meng et al., 2009). The process ends when the increase of homogeneity is below a defined threshold.

A scale parameter is an important factor to determine a limit of change of heterogeneity throughout the segmentation process. It can also decide the average image object size.

Therefore a higher scale parameter will allow for more merging ability in order to get bigger objects, and vice versa (Rahman and Saha, 2008). Partition of images to set useful objects is a fundamental procedure for successful image analysis as well as for image interpretation (Gorte, 1998; Baatz and Schape, 2000; Blaschke et al., 2000). The segment-based classification can effectively avoid the "salt and pepper phenomenon" (Meng et al., 2009).

To prepare the landscape compositional variables for modeling habitat suitability, segmentation-based land use classification was conducted based on fuzzy logic in combination with knowledge rules. First of all, the Landsat image was imported to eCognition and the color composition of the displayed image was changed. Based on the spectral characteristic and spatial resolution of the Landsat image, 14 levels of segmentation were tested to identify the best suitable parameters (Table 8).

Figure 21: An example of level hierarchy in eCongition showing the basic concept of object-oriented image analysis (Definiens, 2003).

Table 8: Separated segmentation processes with various parameters in eCognition 3. The level 8 in red showed the best one for segmentation of this study.

On the basis of prior knowledge of the study area and visual inspection of the number and shape of image objects, the level 8 was selected for the final classification procedure of land cover in eCognition. This Level 8 had a scale parameter of 10, with 70% of the criterion dependent on color and 30% on shape. The later factor was divided between smoothness and compactness, with the criterion dependent 50% and 50%, respectively (see Fig. 22).

After segmentation, the objects were identified as vegetation classes with different colors.

All image objects were classified using a class hierarchy which is based on fuzzy logic.

Each class of classification, the scheme contains a class description. Examples of the conducted procedures of segmentation and classification for this study are illustrated in

Name Scale

Parameter Color Shape Smoothness Compactness

Level 1 8 0,9 0,1 0,9/0.1

Level 2 8 0,7 0,3 0,5/0,5

Level 3 10 0,8 0,2 0,5/0,5

Level 4 10 0,9 0,1 0,5/0,5

Level 5 10 0,9 0,1 0,8/0,2

Level 6 10 0,9 0,1 0,6/0,4

Level 7 10 0,9 0,1 0,9/0,1

Level 8 10 0,7 0,3 0,5/0,5

Level 9 15 0,8 0,2 0,6/0,4

Level 10 20 0,8 0,2 0,5/0,5

Level 11 25 0,8 0,2 0,6/0,4

Level 12 30 0,7 0,3 0,5/0,5

Level 13 30 0,6 0,4 0,5/0,5

Level 14 50 0,7 0,3 0,5/0,5

Figure 22: The scale parameter and composition of homogeneity critera (Screenshot from the segmentation process of eCognition).

Figure 23. The classification scheme had been constructed based on feature classes which are expected to be relevant for tiger ecology. 13 main land use classes were included in the classification process in eCognition. They are evergreen closed forest, evergreen open forest, and evergreen open forest with rattan, secondary forest, agriculture, water, stream bed, hill evergreen forest, hill forest, scrubland, bamboo and shade (missing data). Most of these classes, such as bamboo, evergreen closed forest, evergreen open forest, agriculture, streambed and water are classified based on the spectral color information of the Landsat image. For the remaining classes, training areas (sample areas) were selected based on not only the secondary data sources (ground truth points /existing land use map) but also the reflection values of image segments.

The following expert rules were also used in segmentation-based land use classification to get reliable classification results:

- evergreen closed and evergreen open forest exist up to 900 m.a.s.l. (Kermode, 1964),

- evergreen open forest with rattan can grow between 230 - 365 m.a.s.l. ( depending on the species),

- kaing grass cannot be found in areas which are more than 2.5 km away from water (based on the Kaing Grass Survey conducted by WCS, Myanmar Programme), - Hilly evergreen forest is found in areas of elevation between 900 and 1500 m.a.s.l.

(Kermode, 1964), and,

- Hill forest is found in areas more than 1, 500 m.a.s.l. (Kermode, 1964).

Class related features were also considered. For example, kaing grass was chosen as a class similar to agriculture. For the analysis of segment‟s spectral reflection, a natural color (3, 2, 1) was also used for kaing grass.

The problematic classes were also encountered especially for the classes of agriculture and kaing grass. In this context, manual classification and visual interpretation based on ground truth knowledge were also used. By using a standard false color composite (image bands 4, 5, 3), automatic classification was executed.

Then, the result of segmentation-based classification was exported in shape file format (polygon features). To change to raster format, it was then imported into GIS software

(ArcGIS). The required mapping operations were performed in ArcGIS, such as smoothing long and narrow polygons, digitizing river classes, merging the further required classes and filtering unclassified pixels.

Figure 23: Illustration of segmentation boundaries (1), sample selection (2), class description (3), inputting class related features (4) and comparison between selected classes (5).