Marine debris is a growing threat to our oceans, impacting both wildlife and human activities. Monitoring debris, especially after natural disasters, is crucial but challenging. While Synthetic Aperture Radar (SAR) offers all-weather imaging, its effectiveness is hampered by noise and low contrast. This study proposes a new method for unsupervised SAR image enhancement using Enhanced Multilevel Enhancement (EME). EME improves image quality, allowing for more accurate debris detection compared to existing methods. This approach provides valuable insights into debris distribution across various landscapes, aiding in better understanding and managing this environmental issue.
KEYWORDS: Sensors, Sensor networks, Machine learning, Environmental monitoring, Data modeling, Air quality, Monte Carlo methods, Wind speed, Detection and tracking algorithms, Design
In this paper, we present an optimization methodology for reducing the number of sensors in an existing monitoring network. These sensors measure the concentration of pollutant gas in the air, in order to estimate the position and intensity of a pollutant source. Two statistical methods were used and compared. The first method is based on Hierarchical Agglomerative Clustering (HAC), and the second one is Self-Organized Maps (SOM). The aim is to regroup sensors of the same behavior, based on similarity measure; then, we keep only one sensor of each cluster. The methodology was tested on synthetic data, with Bayesian inference and Monte Carlo Markov Chain (MCMC) algorithm to identify the pollutant source position and intensity. Of 88 sensors in the initial network, the number was reduced to 21 by HAC and 27 by SOM. As for the identification, both methods had close estimation of the source position, however the SOM had better results in the estimation of the source intensity in general.
Iceberg distribution, dispersion, and melting dynamics are pivotal in regulating the Ocean's heat and freshwater balance. However, deciphering these dynamics is a formidable challenge. In visible imagery, icebergs present significant identification challenges due to their variable appearances, which are influenced by many environmental conditions. These variations manifest as differences in color, texture, shape, and size, complicating the accurate discrimination of icebergs from open water or sea ice. Thus, developing reliable detection methods is critical for monitoring iceberg trajectories, disintegration patterns, and their consequent impact on oceanic freshwater influx. The essence of iceberg detection in visible imagery is the ability to differentiate these formations from their surrounding aquatic environment. Iceberg features display a spectrum of visual characteristics shaped by factors such as meteorological conditions, sea states, and the physical properties of the iceberg surfaces. As a result, adaptive imaging techniques are essential for efficacious detection. This study introduces an innovative Adaptive Contrast Enhancement framework meticulously crafted for iceberg detection in visible imagery. Utilizing a parameterized logarithmic model inspired by the Retinex theory, this method enhances the isolation and manipulation of image elements, thereby significantly elevating image quality. Our findings reveal that this technique markedly improves the visibility of icebergs, outshining traditional and contemporary detection methodologies. Furthermore, it affords more profound insights into the dynamic interplay of icebergs within the marine ecosystem.
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