Vegetation, urban terrain and water are considered as the problematic segments in land use and land cover classifications because of confusion factors. These segments are vulnerable to high misclassification level. In addressing these problems, several fundamental issues shall be emphasized: ineffective stand-alone data classification, high investment for data fusions and the need for high frequency of data collection. Thus, this research proposes a classification method consisting of two important components: Three-tier Detection (TTD) and Multi-level Synergy (MLS) after evaluating LiDAR point cloud, aerial photography, Quickbird and Landsat 7 ETM+ images. TTD which is a hierarchical and priority-based data fusion method is used to solve the vegetation and urban terrain classification while MLS, which is a synergy strategy by the utilization of single data and robust learning algorithms is used for water classification. The creation of TTD that has managed to outperform the stand-alone data classification made it a worthwhile investment while for MLS, the usage of single data is capable of meeting the high data collection demand. Both methods started with data processing such as image filtering followed by the comparison of several existing techniques for each data (rank) to identify their potentials and limitations. Next, multi-level data fusions and multi-level synergy are conducted for TTD and MLS, respectively. The dataset employed is Bukit Kanada, Sarawak which exemplifies a coastal mixed-land zone. The performance is then measured using statistical indices include overall accuracy and Kappa Index of Agreement. Both TTD and MLS outperformed recent works such as Normalized Digital Surface Model, Edge Detection technique and Support Vector Machine. Based on the success rates, TTD is suitable to be applied in planning and development sectors, management and detection of land use changes while MLS is suitable for creating maps, charts, and also in monitoring national coastline
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