Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment

publishedVersio

Integration of Hyperspectral and Magnetic Data for Geological Characterization of the Niaqornarssuit Ultramafic Complex in West-Greenland

publishedVersio

Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment

Technological innovations and advanced multidisciplinary research increase the demand for multisensor data fusion in Earth observations. Such fusion has great potential, especially in the remote sensing field. One sensor is often insufficient in analyzing urban environments to obtain comprehensive results. Inspired by the capabilities of hyperspectral and Light Detection and Ranging (LiDAR) data in multisensor data fusion at the feature level, we present a novel approach to the multitemporal analysis of urban land cover in a case study in Høvik, Norway. Our generic workflow is based on bitemporal datasets; however, it is designed to include datasets from other years. Our framework extracts representative endmembers in an unsupervised way, retrieves abundance maps fed into segmentation algorithms, and detects the main urban land cover classes by implementing 2D ResU-Net for segmentation without parameter regularizations and with effective optimization. Such segmentation optimization is based on updating initial features and providing them for a second iteration of segmentation. We compared segmentation optimization models with and without data augmentation, achieving up to 11% better accuracy after segmentation optimization. In addition, a stable spectral library is automatically generated for each land cover class, allowing local database extension. The main product of the multitemporal analysis is a map update, effectively detecting detailed changes in land cover classes

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

publishedVersio

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense

Metabolic syndrome is associated with similar long-term prognosis in non-obese and obese patients:An analysis of 45 615 patients from the nationwide LIPIDOGRAM 2004-2015 cohort studies

Aims: We aimed to evaluate the association between metabolic syndrome (MetS) and long-term all-cause mortality. Methods and results: The LIPIDOGRAM studies were carried out in the primary care in Poland in 2004, 2006, and 2015. MetS was diagnosed based on the National Cholesterol Education Program, Adult Treatment Panel III (NCEP/ATP III), and Joint Interim Statement (JIS) criteria. The cohort was divided into four groups: non-obese patients without MetS, obese patients without MetS, non-obese patients with MetS, and obese patients with MetS. Differences in all-cause mortality were analysed using Kaplan-Meier and Cox regression analyses. A total of 45 615 participants were enrolled (mean age 56.3, standard deviation: 11.8 years; 61.7% female). MetS was diagnosed in 14 202 (31%) by NCEP/ATP III criteria and 17 216 (37.7%) by JIS criteria. Follow-up was available for 44 620 (97.8%, median duration 15.3 years) patients. MetS was associated with increased mortality risk among the obese {hazard ratio, HR: 1.88 [95% confidence interval (CI) 1.79-1.99] and HR: 1.93 [95% CI 1.82-2.04], according to NCEP/ATP III and JIS criteria, respectively} and non-obese individuals [HR: 2.11 (95% CI 1.85-2.40) and 1.7 (95% CI 1.56-1.85) according to NCEP/ATP III and JIS criteria, respectively]. Obese patients without MetS had a higher mortality risk than non-obese patients without MetS [HR: 1.16 (95% CI 1.10-1.23) and HR: 1.22 (95% CI 1.15-1.30), respectively in subgroups with NCEP/ATP III and JIS criteria applied]. Conclusions: MetS is associated with increased all-cause mortality risk in non-obese and obese patients. In patients without MetS, obesity remains significantly associated with mortality. The concept of metabolically healthy obesity should be revised. © 2023 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved

Metabolic syndrome is associated with similar long-term prognosis in non-obese and obese patients. An analysis of 45 615 patients from the nationwide LIPIDOGRAM 2004-2015 cohort studies

AIMS: We aimed to evaluate the association between metabolic syndrome (MetS) and long-term all-cause mortality. METHODS: The LIPIDOGRAM studies were carried out in the primary care in Poland in 2004, 2006 and 2015. MetS was diagnosed based on the National Cholesterol Education Program, Adult Treatment Panel III (NCEP/ATP III) and Joint Interim Statement (JIS) criteria. The cohort was divided into four groups: non-obese patients without MetS, obese patients without MetS, non-obese patients with MetS and obese patients with MetS. Differences in all-cause mortality was analyzed using Kaplan-Meier and Cox regression analyses. RESULTS: 45,615 participants were enrolled (mean age 56.3, standard deviation: 11.8 years; 61.7% female). MetS was diagnosed in 14,202 (31%) by NCEP/ATP III criteria, and 17,216 (37.7%) by JIS criteria. Follow-up was available for 44,620 (97.8%, median duration 15.3 years) patients. MetS was associated with increased mortality risk among the obese (hazard ratio, HR: 1.88 [95% CI, 1.79-1.99] and HR: 1.93 [95% CI 1.82-2.04], according to NCEP/ATP III and JIS criteria, respectively) and non-obese individuals (HR: 2.11 [95% CI 1.85-2.40] and 1.7 [95% CI, 1.56-1.85] according to NCEP/ATP III and JIS criteria respectively). Obese patients without MetS had a higher mortality risk than non-obese patients without MetS (HR: 1.16 [95% CI 1.10-1.23] and HR: 1.22 [95%CI 1.15-1.30], respectively in subgroups with NCEP/ATP III and JIS criteria applied). CONCLUSIONS: MetS is associated with increased all-cause mortality risk in non-obese and obese patients. In patients without MetS obesity remains significantly associated with mortality. The concept of metabolically healthy obesity should be revised