ACADSTAFF UGM
| Title | : | Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach |
| Author | : |
Dr. Nur Mohammad Farda, S.Si., M.Cs. (1) Prof. Drs. Projo Danoedoro, M.Sc., Ph.D. (2) Prof. Dr. Hartono, DEA., DESS. (3) Prof. Drs. Agus Harjoko, M.Sc., Ph.D. (4) |
| Date | : | 14 2016 |
| Keyword | : | Image Mining,Remote Sensing,Coastal Wetlands Image Mining,Remote Sensing,Coastal Wetlands |
| Abstract | : | The availably of remote sensing image data is numerous now, and with a large amount of data, it makes the "knowledge gap" in the extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using a pixel-based and object-based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in the Segara Anakan lagoon are selected to represent data at various multi-temporal images. The inputs for image mining are visible and near-infrared bands, PCA band, inverse PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There are three methods were applied to extract coastal wetlands using image mining: pixel-based - Decision Tree C4.5, pixel-based - Back Propagation Neural Network, and object-based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map the coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with the highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide a better understanding of their spatiotemporal coastal wetlands dynamics distribution. |
| Group of Knowledge | : | |
| Level | : | Internasional |
| Status | : |
Published
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