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Object oriented classification for land cover mapping


The pixel-based classification results, which only based on the spectral mean of the digital number itself, is no way to differentiate cloud with urban and clear land completely [see Figure 3, part (d)]. But if look carefully on high land vegetation area at Figure 3, part (f), it can be clearly seen that cloud is no more mix classified, neither with urban area nor bare or clear land. In e-Cognition, the classification is not only based on the spectral number itself, it can accept other source regardless of its' data properties (8 bits, 16 bits or 32 bits). In this case, the cloud is clearly classified by using the DEM band (16 bits) as the main parameter where in generally no urban area or clear land can be found at certain height especially at hilly area where its' surrounding area cover by dense typical forest. Due to this, by setting the parameter at the urbanization area can only be found lower than 440 m from the sea level only as shown in Figure 4.


Figure 4: Setting the DEM parameter to determine the urbanization area


Post Classification Analysis
In order to make a direct comparison of accuracy assessment between the pixel-based and polygon-based classification results, the accuracy assessment has been carried out in the same environment. The program automatically picks out 300 random sample points plus 15 ground truth points for accuracy assessment. The statistic result is shown in Table 3. From the results, the overall accuracy has shown the higher accuracy in polygon-based classification result.

Table 2: Accuracy assessment
Accuracy statistics Pixel-based (%) Polygon-based (%)
Forest 81.507 94.972
Water body 94.118 100.00
Urban Area 88.889 86.111
Bare or Clear Land 72.727 83.333
Orchard 75.000 84.000
Rubber and Scrub 84.906 82.353
Cloud 83.333 100.000
Shadow 33.333 100.000
Overall Accuracy 81.667 90.667

Conclusion
In this paper, the object oriented analysis technique has been introduced for classification and the result is satisfied for land cover mapping. The proposed technique was successfully tested with Landsat TM image. The results presented in this paper show the efficiency and higher accuracy for polygon-based classification. This technique is recommended to test on VHR data such as Ikonos image or Aerial photos especially in town area where more details classes can be generated.

References
  • Ambiente Humano, 2000. "eCogntition and Change Detection-Integrating Aerial Photos and Satellite Images." http://www.definins.com. eCognition Application Notes, Vol. 1, No. 2, September 2000.
  • Emily Wilson and Dan Civco, 2002. "Research on Improve Land Use Information Derived from Landsat and IKONOS". Laboratory for Earth Resources Information Systems, Department of Natural Resources Management and Engineering, The University of Connecticut, USA.
  • Ioannis Manakos, 2001, "eCognition and Precision Farming." http://www.lrz-muenchen.de/~lnn/. eCognition Application Notes, Vol. 2, No 2, April 2001.
  • Martin Baatz, Markus Heynen, Peter Hofmann, Iris Lingenfelder, Martthias Mimier, Amo Schape, Michaela Weber and Gregor Willhauck, 2001. "eCognition User Guide 2.0 : Object Oriented Image Analysis." Definiens Imaging GmbH, Trappentreustrasse 1, 80339 München, Germany.
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