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Comparison Effectiveness of Pixel Based Classification and Object Based Classification Using High Resolution Image In Floristic Composition Mapping (Study Case: Gunung Tidar Magelang City)
| Content Provider | Scilit |
|---|---|
| Author | Aryaguna, Prama Ardha Danoedoro, Projo |
| Copyright Year | 2016 |
| Description | Journal: Iop Conference Series: Earth and Environmental Science Developments of analysis remote sensing have same way with development of technology especially in sensor and plane. Now, a lot of image have high spatial and radiometric resolution, that's why a lot information. Vegetation object analysis such floristic composition got a lot advantage of that development. Floristic composition can be interpreted using a lot of method such pixel based classification and object based classification. The problems for pixel based method on high spatial resolution image are salt and paper who appear in result of classification. The purpose of this research are compare effectiveness between pixel based classification and object based classification for composition vegetation mapping on high resolution image Worldview-2. The results show that pixel based classification using majority 5×5 kernel windows give the highest accuracy between another classifications. The highest accuracy is 73.32% from image Worldview-2 are being radiometric corrected level surface reflectance, but for overall accuracy in every class, object based are the best between another methods. Reviewed from effectiveness aspect, pixel based are more effective then object based for vegetation composition mapping in Tidar forest. |
| Related Links | http://iopscience.iop.org/article/10.1088/1755-1315/47/1/012042/pdf |
| ISSN | 17551307 |
| e-ISSN | 17551315 |
| DOI | 10.1088/1755-1315/47/1/012042 |
| Journal | Iop Conference Series: Earth and Environmental Science |
| Volume Number | 47 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2016-11-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Earth and Environmental Science Imaging Science Remote Sensing Floristic Composition Object Based Classification and Object Pixel Based Classification |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Physics and Astronomy Environmental Science |
