Fusion of Hyperspectral Images and Lidar-based Dems for Coastal Mapping

Coastal mapping is essential for a number of applications such as coastal resource management, coastal environmental protection, and coastal development and planning. Coastal mapping has been carried out using a wide range of techniques such as ground surveying and aerial mapping. Recently, satellite images, active sensor elevation models, and multispectral and hyperspectral images have also been used in coastal mapping. The integration of two or more of these datasets can provide more reliable coastal information. This paper presents an alternative technique for coastal mapping using an AVIRIS image and a LIDAR-based DEM. The DEM is used to generate building cues that are converted to building polygons. Building pixels are then removed from the AVIRIS image, and a supervised classification is performed to generate road and shoreline classes. A number of image processing techniques are used to victories road and shoreline pixels. The geometric accuracy and the completeness of the results are evaluated. The average positional accuracy for the building, road, and shoreline layers are 2.3, 5.7 and 7.2 meters, with 93.2%, 91.3%, and 95.2% detection rates respectively. The results demonstrate the potential of using LIDAR-based DEMs to detect building cues and remove their corresponding pixels from the classification process. Thus, integrating laser and optical data can provide high quality coastal geospatial information.