On the Exploitation of Multimodal Remote Sensing Data Combination for Mesoscale/Submesoscale Eddy Detection in the Marginal Ice Zone

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. As one of the steps toward this aim, we have investigated the pertinence of applying the scheme, including a texture features extraction and a superpixel segmentation method, in order to distinguish eddies in the marginal ice zone (MIZ) using multisensor remote sensing data. Nevertheless, not all the images available from various sensors are of actual importance, since they can be corrupted, redundant, or simply unnecessary for a particular task. Therefore, we are additionally exploring the relevance of different sensors separately and simultaneously as well as with extracted texture features for eddy monitoring

A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization

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 and efficient method based on graph Laplacians for information selection at different levels of data fusion. The proposed approach combines data structure and information content to address the limitations of existing graph-Laplacian-based methods in dealing with heterogeneous datasets. Moreover, it adapts the selection to each homogenous area of the considered images according to their underlying properties. Experimental tests carried out on several multivariate remote sensing datasets show the consistency of the proposed approach

SAR and Passive Microwave Fusion Scheme: A Test Case on Sentinel-1/AMSR-2 for Sea Ice Classification

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 classification employing SAR and PMR separately and simultaneously, in order to evaluate the complementarity of both sensors and to assess the result of a combined use. Our test case illustrates the flexibility and efficiency of the proposed scheme and indicates an advantage of combining AMSR-2 89 GHz and Sentinel-1 data for sea ice mapping

Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification

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 set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step

On the Exploitation of Heterophily in Graph-Based Multimodal Remote Sensing Data Analysis

Source at https://ceur-ws.org/.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. Second, real-world data can show both homophily (’birds of a feather flock together’) and heterophily (’opposites attract’) during propagation, while standard algorithms only consider homophily. Both shortcomings are addressed in this work and the result is a graph-based label propagation algorithm for multimodal data that includes homophily and/or heterophily. Furthermore, the method is also able to transfer information between uni- and multimodal data. Experiments on the remote sensing data set of Houston, which contains a LiDAR and a hyperspectral image, show that our approach ties state-of-the-art methods for classification with an OA of 91.4%, while being more flexible and not constrained to a specific data set or a specific combination of modalities

Multimodal Integrated Remote Sensing for Arctic Sea Ice Monitoring

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 the continuous decline of sea ice in the Arctic the presence of human-based activities will grow. Therefore, reliable information about sea ice conditions is of primary interest to protect the Arctic and to ensure safe and effective commercial activities and polar navigation. Currently, sea ice services produce operational ice charts manually using the knowledge of sea ice experts. However, with an increasing number of various data sources that provide different information regarding sea ice, it is important to develop automatic methods for sea ice characterization. Robust and automatic ice charting can not be achieved using only one satellite mission. It is fundamental to combine information from various remote sensors with different characteristics for more reliable sea ice monitoring and characterization. However, how do we know that all the information is actually relevant? It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms from the required processing time and accuracy point of view. Therefore, it is crucial to select an optimal set of features that provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. The work in this dissertation specifically focuses on the development of such a method. In this thesis, we employ a fully automatic, flexible, accurate, efficient, and interpretable information selection method that is based on the graph Laplacians. The proposed approach assesses relevant information on a global and local level using two metrics simultaneously and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. Moreover, it is linked in a common scheme with a classification algorithm that helps to properly evaluate the performance of the information selection and provides sea ice classification maps as an output. Accordingly, in recent studies, we investigate and evaluate the robustness and effectiveness of the proposed method for sea ice classification by testing several data combinations with various sea ice conditions. Experiments illustrate the flexibility and efficiency of the proposed scheme and clearly indicate an advantage of combining various sensors. Moreover, the results demonstrate the potential for operational sea ice monitoring that should be further thoroughly examined in future studies

Eddy Detection in the Marginal Ice Zone with Sentinel-1 Data Using YOLOv5

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 specifically collected and labeled high-resolution synthetic aperture radar data for a very dynamic area over the Fram Strait. Our approach involved fine-tuning pre-trained YOLOv5 models on a sparse dataset and achieved accurate results with minimal training data. The performances of the models were evaluated using several metrics, and the best model was selected by visual examination. The experimental results obtained from the validation and test datasets consistently demonstrated the robustness and effectiveness of the chosen model to identify submesoscale and mesoscale eddies with different structures. Moreover, our work provides a foundation for automated eddy detection in the marginal ice zone using synthetic aperture radar imagery and contributes to advancing oceanography research

Selecting principal attributes in multimodal remote sensing for sea ice characterization

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 to several types of data without loss of physical interpretability. Considering that different homogeneous regions require different types of information, we perform the selection patch-wise. Accordingly, by exploiting the spatial information, we increase the robustness and accuracy of PFA