Airborne hyperspectral imaging for multisensor data fusion

• Main Author: Kuras, Agnieszka
• Other Authors: Burud, Ingunn, Brell, Maximilian, Rogass, Christian, Thiis, Thomas Kringlebotn
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Norwegian University of Life Sciences, Ås 2023
• Subjects: multisensor data fusion; hyperspectral imaging; LiDAR; urban environment; harsh environment; data integration; deep learning; machine learning; Greenland
• Online Access: https://hdl.handle.net/11250/3053612

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 ...