scholarly journals Optical Remote Sensing of Oil Spills in the Ocean: What Is Really Possible?

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chuanmin Hu ◽  
Yingcheng Lu ◽  
Shaojie Sun ◽  
Yongxue Liu
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Current Literature ◽  
Oil Spills ◽  
Numerical Models ◽  
Free Water ◽  
Optical Remote Sensing ◽  
Laboratory Measurements ◽  
Remote Sensing Imagery ◽  
Sun Light

Optical remote sensing (ORS) of reflected sun light has been used to assess oil spills in the ocean for several decades. While most applications are toward simple presence/absence detections based on the spatial contrast between oiled water and oil-free water, recent advances indicate the possibility of classifying oil types and quantifying oil volumes based on their spectral contrasts with oil-free water. However, a review of the current literature suggests that there is still confusion on whether this is possible and, if so, how. Here, based on the recent findings from numerical models, laboratory measurements, and applications to satellite or airborne imagery, we attempt to clarify this situation by summarizing (1) the optics behind oil spill remote sensing, and in turn, (2) how to interpret optical remote sensing imagery based on optical principles. In the end, we discuss the existing limitations and challenges as well as pathways forward to advance ORS of oil spills.

OIL SPILL DETECTION WITH REMOTE SENSORS

2016 ◽  
Author(s):  
Kristina Pilžis ◽  
Vaidotas Vaišis
Keyword(s):  
Remote Sensing ◽  
Surface Properties ◽  
Oil Spill ◽  
Marine Environment ◽  
Oil Spills ◽  
Sea Surface ◽  
Optical Remote Sensing ◽  
Oil Spill Detection ◽  
Remote Sensors ◽  
Microwave Sensors

Accurate detection and forecasting of oil spills and their trajectories is beneficial for monitoring and conservation of the marine environment. The most common techniques of oil spill tracking are remote sensing from an aircraft and satellites. Remote sensors work by detecting sea surface properties and the most effective of them are laser fluorosensors, optical remote sensing (visible, infrared, ultraviolet) and microwave sensors. Possibilities and advantages of their use are reviewed in this article.

scholarly journals Boundary-Assisted Learning for Building Extraction from Optical Remote Sensing Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 760
Author(s):  
Sheng He ◽  
Wanshou Jiang
Keyword(s):  
Remote Sensing ◽  
Receptive Fields ◽  
Morphological Characteristics ◽  
Learning Task ◽  
Aerial Image ◽  
Model Parameters ◽  
Optical Remote Sensing ◽  
Building Extraction ◽  
Convolutional Network ◽  
Remote Sensing Imagery

Deep learning methods have been shown to significantly improve the performance of building extraction from optical remote sensing imagery. However, keeping the morphological characteristics, especially the boundaries, is still a challenge that requires further study. In this paper, we propose a novel fully convolutional network (FCN) for accurately extracting buildings, in which a boundary learning task is embedded to help maintain the boundaries of buildings. Specifically, in the training phase, our framework simultaneously learns the extraction of buildings and boundary detection and only outputs extraction results while testing. In addition, we introduce spatial variation fusion (SVF) to establish an association between the two tasks, thus coupling them and making them share the latent semantics and interact with each other. On the other hand, we utilize separable convolution with a larger kernel to enlarge the receptive fields while reducing the number of model parameters and adopt the convolutional block attention module (CBAM) to boost the network. The proposed framework was extensively evaluated on the WHU Building Dataset and the Inria Aerial Image Labeling Dataset. The experiments demonstrate that our method achieves state-of-the-art performance on building extraction. With the assistance of boundary learning, the boundary maintenance of buildings is ameliorated.

Optimal Detection and Estimation of Marine Oil Spills Through Coherent Pluralism

2013 ◽  
Vol 4 (1) ◽  
pp. 84-111
Author(s):  
Kufre Bassey ◽  
Polycarp Chigbu
Keyword(s):  
Remote Sensing ◽  
Environmental Science ◽  
Oil Spills ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Marine Oil ◽  
Detection And Estimation ◽  
Number Of False Alarms ◽  
Marine Oil Spills ◽  
Remote Sensing Techniques

An important area of environmental science involves the combination of information from diverse sources relating to a similar endpoint. Majority of optical remote sensing techniques used for marine oil spills detection have been reported lately of having high number of false alarms (oil slick look-a-likes) phenomena which give rise to signals which appear to be oil but are not. Suggestions for radar image as an operational tool has also been made. However, due to the inherent risk in these tools, this paper presents the possible research directions of combining statistical techniques with remote sensing in marine oil spill detection and estimation.

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