مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

1,821
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

0
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

2

Information Journal Paper

Title

COMPARISON OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST TYPE MAPPING USING SATELLITE DATA

Pages

  869-881

Abstract

 There are various methods for classifying phenomena in satellite images. Conventional methods of CLASSIFICATION are PIXEL-BASEd. Satellite images may also be classified using OBJECT-BASEd methods. In this method, a group of pixels that form the phenomenon are selected. In order to compare PIXEL-BASEd and OBJECT-BASEd methods in distinguishing forests types, this research was conducted in Forest Research Station of Tehran University in the central Caspian forests. The Landsat 7 ETM++ image was analysed. First, a precise orthorectification was done. Then, enhancement techniques, including PCA, Tesseled cap, and rationing, were employed. In the PIXEL-BASEd method the Maximum likelihood classifier was used and the FOREST TYPEs classified were pure beech, mixed beech, pure hornbeam, mixed hornbeam, mixed alder, mixed and plantation areas. In object-oriented approach, three CLASSIFICATION methods of nearest neighbour, MEMBERSHIP FUNCTION, and an integrating of both methods were used. In each method the best segmentation parameters were applied in order to extract the homogenous area as a FOREST TYPE. By nearest neighbour method, after segmentation, some objects in each type were selected as training objects. By MEMBERSHIP FUNCTION method, CLASSIFICATION was done by three steps and segmentation levels. At each level, FOREST TYPEs hierarchically were extracted by determining the best fuzzy logic and function. The third method (combined of two first methods) was performed by four segmentation and CLASSIFICATION levels. To generate a ground truth map of forest general types, a systematic random sampling method with 193 plots with one hectare area was done in the forest. In each plot, FOREST TYPE was determined by computing tree species frequencies using two methods: total number of each species and, a frequency of each species in 100 thick tree classes. The accuracy assessment of FOREST TYPE maps showed that the object-oriented CLASSIFICATION approach considerably improved the results comparing with PIXEL-BASEd CLASSIFICATION approach (from 25.5% to 44.4%). The study also indicated that the combined nearest neighbour and MEMBERSHIP FUNCTION methods could improve the results over the other techniques.

Cites

References

Cite

APA: Copy

SHATAEI JOUYBARI, SH., DARVISH SEFAT, A.A., & SOBHANI, H.. (2007). COMPARISON OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST TYPE MAPPING USING SATELLITE DATA. IRANIAN JOURNAL OF NATURAL RESOURCES, 60(3), 869-881. SID. https://sid.ir/paper/22942/en

Vancouver: Copy

SHATAEI JOUYBARI SH., DARVISH SEFAT A.A., SOBHANI H.. COMPARISON OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST TYPE MAPPING USING SATELLITE DATA. IRANIAN JOURNAL OF NATURAL RESOURCES[Internet]. 2007;60(3):869-881. Available from: https://sid.ir/paper/22942/en

IEEE: Copy

SH. SHATAEI JOUYBARI, A.A. DARVISH SEFAT, and H. SOBHANI, “COMPARISON OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST TYPE MAPPING USING SATELLITE DATA,” IRANIAN JOURNAL OF NATURAL RESOURCES, vol. 60, no. 3, pp. 869–881, 2007, [Online]. Available: https://sid.ir/paper/22942/en

Related Journal Papers

Related Seminar Papers

  • No record.
  • Related Plans

  • No record.
  • Recommended Workshops






    Move to top