Airborne hyperspectral imaging for multisensor data fusion

Multisensor data fusion demand in Earth observations is constantly increasing thanks to technological advances and the willingness to explore the Earth in a multidisciplinary way. Recently hyperspectral imaging has become a promising tool for Earth monitoring purposes but has also emerged as suitable for fusion with other remote sensors for various applications. This dissertation examines different types of multisensor data fusion, such as feature-level and application-level fusion, where each application is based on hyperspectral imaging at the airborne scale. In feature-level data fusion, hyperspectral imaging is combined with LiDAR (Light Detection and Ranging) to analyze urban environments, mainly focusing on urban land cover classification and implementing deep learning algorithms. In contrast, application-level data fusion presents the integration of hyperspectral imaging with magnetic data for material characterization of geologic complexes in remote and harsh environments, such as Greenland. This PhD thesis focused on enhancing analysis outcomes by combining hyperspectral imaging with other sensors and precisely selecting applications in which one sensor is insufficient to obtain the required parameters. The analysis of feature-level data fusion for hyperspectral and LiDAR data began with a detailed review of sensor key characteristics most representative of urban land cover analysis. These features were intended to segment land cover classes by considering 2D and 3D convolutional operations, where 2D convolutions involve spatial information and 3D convolutions add a spectral dimension allowing the inclusion of information about the interrelation of hyperspectral bands. The study on feature-level data fusion was completed with a multitemporal analysis, where a general framework was proposed towards automatical updating a local urban database. The other part of the dissertation was based on the fusion of sensors operating in different feature vectors with a common factor: identifying iron and its magnetic properties. Iron in hyperspectral imaging also has distinct absorption features recognizable at the relatively low spatial resolution. Moreover, it is the only chemical element capable of maintaining magnetic properties, which is the main aim of magnetic surveys. This dissertation has contributed new approaches to various feature-level and application-level multisensor data fusion exploitations confirming the great potential and versatility and showing future directions of multidisciplinary research using remote sensing methods for Earth observation. This work is part of the project “FKB maskinlæring” funded byRFF “Oslo og Akershus Regionale forskningsfond” (295836). In addition, the research was funded by the EnMAP scientific preparation program under the Space Agency at DLR with resources from the German Federal Ministry of Economic Affairs and Climate Action, grant number 50EE1529.