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Object oriented classification for land cover mapping


Methodology


Figure 1: Schematic diagram illustrates the object oriented image analysis flow chart in eCognition.


Basically, the process can be divided into three simple steps. After bring the image into eCognition, the image will be applied the multiresolution segmentation. After satisfied with the segmentation as shown in level 2 from Figure 2, few general classes are created and applied standard nearest neighbours, this segments are then pick out randomly for training samples, basic supervised classification can be proceeded to obtain an absolute general classified classes (2nd step). For example: urbanization area, vegetation area, water body and etc. Once the classified general classes is acceptable, further classification process (3rd step) can be carried out to generate the desire classes, for example, urban area and clear land from urbanization area main class; rubber, oil palm, scrub and grass land from vegetation area main class. These child classes can be generated by its' full range of fuzzy logic functions availability. Table 1 shows the general differences between conventional pixel-based classification and new approach of polygon based classification.

Table 1: Differences Between Pixel Based and Object Oriented Classification
Pixel-Based Classification Object Oriented Classification
Correct the atmospheric distortion
Require gain and offset values, sun elevation angle, ground visibility and etc.		 Make segmentation
Directly apply onto the image until the desire object segmentation polygon has appeared.
Based on the spectral mean of each band that contain in the image Besides the total bands available in the image, DEM band, brightness or vectors can be undertaken for classification parameters.
Classification is made at one time only Step by step classification can be applied onto the image. The classification step can always be carried out from time to time in order to classify the classes from minimum of two classes to more.
Apply the mode filter to reduce the distortion. No filter is needed because the image has already in meaningful polygon after segmentation step.

Results and discussion


Figure 2: Hierarchical net of image objects derived from image segmentation level 1 (5 pixels scale parameter), level 2 (15 pixels scale parameter) and level 3 (30 pixels scale parameter)


In this paper, two different areas have been chosen for testing the new technique. The first one is located at urbanization area as seen in part (a), Figure 3. The other one is located at high land, vegetation area, which surrounded by dense typical forest as seen in part (b), Figure 3. The results of pixel based and polygon-based classification has been compared. It is shown in part (c), (d), (e) and (f), Figure 3.


Figure 3: Landsat TM (Band 4, 5, 3) testing area (a) Urbanization Area; (b) High Land Vegetation Area. Comparison between pixel based and polygon based classification (c) pixel-based at urbanization area; (d) pixel-based at vegetation area; (e) polygon-based at urbanization area; (f) polygon-based at vegetation area

The parts (c) and (d) from Figure 4 are the results pixel-based maximum likelihood supervised classification. As a result, the classified image produced salt and pepper image or lot of small clumps (< 10 pixels) appeared in the classified image. Vice versa, the classified image derived from polygon-based classification is closer to human visual interpretation.

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