English
norskBlar i forfatter "Khachatrian, Eduard"
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Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-26)It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal ... -
Eddy Detection in the Marginal Ice Zone with Sentinel-1 Data Using YOLOv5
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-04-24)The automatic detection and analysis of ocean eddies in the marginal ice zone via remote sensing is a very challenging task but of critical importance for scientific applications and anthropogenic activities. Therefore, as one of the first steps toward the automation of the eddy detection process, we investigated the potential of applying YOLOv5, a deep convolutional neural network architecture, to ... -
Exploring the Potential of Sentinel-1 Ocean Wind Field Product for Near-Surface Offshore Wind Assessment in the Norwegian Arctic
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-01-24)The exploitation of offshore wind resources is a crucial step towards a clean energy future. It requires an advanced approach for high-resolution wind resource evaluations. We explored the suitability of the Sentinel-1 Level-2 OCN ocean wind field (OWI) product for offshore wind resource assessments. The SAR data were compared to in situ observations and three reanalysis products: the global ... -
A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-13)When dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provide more details on modeling the physical phenomena occurring on the Earth’s surface. In this article, we introduce a flexible ... -
Multimodal Integrated Remote Sensing for Arctic Sea Ice Monitoring
(Doctoral thesis; Doktorgradsavhandling, 2023-06-15)Remote sensing data acquired from various sensors have been used for decades to monitor sea ice conditions over polar regions. Sea ice plays an essential role in the polar environment and climate. Furthermore, sea ice affects anthropogenic activities, including shipping and navigation, the oil and gas industry, fisheries, tourism, and the lifestyle of the indigenous population of the Arctic. With ... -
On the Exploitation of Heterophily in Graph-Based Multimodal Remote Sensing Data Analysis
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022)The field of Earth observation is dealing with increasingly large, multimodal data sets. An important processing step consists of providing these data sets with labels. However, standard label propagation algorithms cannot be applied to multimodal remote sensing data for two reasons. First, multimodal data is heterogeneous while classic label propagation algorithms assume a homogeneous network. ... -
On the Exploitation of Multimodal Remote Sensing Data Combination for Mesoscale/Submesoscale Eddy Detection in the Marginal Ice Zone
(Journal article; Tidsskriftartikkel, 2022-10-17)The detection and analysis of ocean eddies via remote sensing have become a hot topic in physical oceanography during the last few decades. However, eddy identification and tracking via remote sensing can be a challenging task, since each sensor has some limitations. In order to overcome potential challenges, it is crucial to exploit the complementary information provided by different sensing systems. ... -
SAR and Passive Microwave Fusion Scheme: A Test Case on Sentinel-1/AMSR-2 for Sea Ice Classification
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-02-14)The most common source of information about sea ice conditions is remote sensing data, especially images obtained from synthetic aperture radar (SAR) and passive microwave radiometers (PMR). Here we introduce an adaptive fusion scheme based on Graph Laplacians that allows us to retrieve the most relevant information from satellite images. In a first test case, we explore the potential of sea ice ... -
Selecting principal attributes in multimodal remote sensing for sea ice characterization
(Chapter; Bokkapittel, 2021)Automatic ice charting cannot be achieved using only SAR modalities. It is fundamental to combine information from other remote sensors with different characteristics for more reliable sea ice characterization. In this paper, we employ principal feature analysis (PFA) to select significant information from multimodal remote sensing data. PFA is a simple yet very effective approach that can be applied ... -
Unsupervised Band Selection for Hyperspectral Datasets by Double Graph Laplacian Diagonalization
(Journal article; Tidsskriftartikkel, 2021)The vast amount of spectral information provided by hyperspectral images can be useful for different applications. However, the presence of redundant bands will negatively affect application performance. Therefore, it is crucial to select a relevant subset that preserves the information of the original set. In this paper, we present an automatic and accurate band selection method based on Graph ...
