An Automated Approach of Shoreline Detection Applied to Digital Videos using Data Mining

This study aims to detect a shoreline location and its changes automatically in the temporal resolution. This approach is implemented on the coastal video monitoring system applications. The proposed method applied data mining by using two main systems-a training system using classification and shoreline detection systems with Self-Organizing Map (SOM) and K-Nearest Neighbor (K-NN) algorithms. The training system performs feature texture extraction using agray-level co-occurrence matrix and the results are stored to classification process. The detection system has five processing stages: contrast stretching preprocessing and morphological contrast enhancement, SOM clustering, morphological operations, feature extraction and K-NN classification and detection shoreline. Preprocessing was used to improve the video image contrast and reliability. SOM algorithm in segmenting objects in the onshore video images. Morphological operations were applied to eliminate noise on the objects that were not needed in the spatial domain. The segmentation results of video frames classified by K-NN. The aim is to provide the class labels on each region segmentation results, namely, sea label, land label and sky label. The determination of the shoreline is done by scanning the neighboring pixels from the edge of land class label after binary image transformation. The shoreline change detection was performed by comparing the position of existing shoreline and shoreline position in the reference video frame. A Receiver Operating Characteristic (ROC) curve was used to evaluate the performance of shoreline detection systems. The results showed that the combination of SOM and K-NN was able to detect shoreline and its changes accurately.