Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Integration of Hyperspectral Imagery and Sparse Sonar Data for Shallow Water Bathymetry Mapping
Accurate and rapid mapping of shallow water bathymetry is essential for the safe operation of many industries. Here, we propose a new approach to shallow water bathymetry mapping that integrates hyperspectral image and sparse sonar data. Our approach includes two main steps: dimensional reduction of Hyperion images and interpolation of sparse sonar data. First, we propose a new algorithm, i.e., a sonar-based semisupervised Laplacian eigenmap (LE) using both spatial and spectral distance, for dimensional reduction of Hyperion imagery. Second, we develop a new algorithm to interpolate sparse sonar points using a 3-D information diffusion method with homogeneous regions. These homogeneous regions are derived from the segmentation of the dimensional reduction results based on depth. We conduct the experimental comparison to confirm the applicability of the dimensional reduction and interpolation methods and their advantages over previously described methods. The proposed dimensional reduction method achieves better dimensional results than unsupervised method and semisupervised LE method (using only spectral distance). Furthermore, the bathymetry retrieved using the proposed method is more precise than that retrieved using common interpolation methods.
Integration of Hyperspectral Imagery and Sparse Sonar Data for Shallow Water Bathymetry Mapping
Accurate and rapid mapping of shallow water bathymetry is essential for the safe operation of many industries. Here, we propose a new approach to shallow water bathymetry mapping that integrates hyperspectral image and sparse sonar data. Our approach includes two main steps: dimensional reduction of Hyperion images and interpolation of sparse sonar data. First, we propose a new algorithm, i.e., a sonar-based semisupervised Laplacian eigenmap (LE) using both spatial and spectral distance, for dimensional reduction of Hyperion imagery. Second, we develop a new algorithm to interpolate sparse sonar points using a 3-D information diffusion method with homogeneous regions. These homogeneous regions are derived from the segmentation of the dimensional reduction results based on depth. We conduct the experimental comparison to confirm the applicability of the dimensional reduction and interpolation methods and their advantages over previously described methods. The proposed dimensional reduction method achieves better dimensional results than unsupervised method and semisupervised LE method (using only spectral distance). Furthermore, the bathymetry retrieved using the proposed method is more precise than that retrieved using common interpolation methods.
Integration of Hyperspectral Imagery and Sparse Sonar Data for Shallow Water Bathymetry Mapping
Liang Cheng (Autor:in) / Lei Ma / Wenting Cai / Lihua Tong / Manchun Li / Peijun Du
2015
Aufsatz (Zeitschrift)
Englisch
Lokalklassifikation TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
Bathymetry Retrieval from Hyperspectral Remote Sensing Data in Optical-Shallow Water
| Online Contents | 2014
SHALLOW-WATER BATHYMETRY WITH COMMERCIAL SATELLITE
| British Library Online Contents | 2006
Coastal Bathymetry from Hyperspectral Observations of Water Radiance
| Online Contents | 1998