Toward Robust Segmentation Results Based on Fusion Methods for Very High Resolution Optical Image and LiDAR Data

Using very high resolution remote sensing images to extracting urban features from very high resolution remote sensing images is a very complex and difficult task. The improvement in geospatial technologies brought forward many solutions that can help in improving the process of urban feature extraction. Data collection using light detection and ranging (LiDAR) and capturing very high resolution optical images concurrently is one of these solutions. This research proves that the fusion of high-resolution optical image with LiDAR data can improve image processing results. It is based on increasing urban features extraction success rate by reducing oversegmentation. The fusion process relies first on wavelet transform techniques, which are run several times with different parameters (rules). Then, an innovative technique is implemented to improve fusion process. The two techniques are compared, and both have reduced fragmented segments and created homogeneous urban features. However, the fused image with the innovative technique has improved the accuracy of the segmentation results. The average accuracy for building detection is 96% (maximum 100% and minimum 92%) using the innovative technique compared to 21% and 51% for no fusion and wavelet-fusion-based techniques. Furthermore, an index is used to measure the quality of the building details which are detected after using the innovative fusion technique. The result indicates that the quality index is greater or equal to 86%.