Oil spills lead to catastrophic problems. In most oil spill cases, the spatial and temporal intractability of the detriment cannot be neglected, and problems related to economic, social and environmental factors constantly appear for a long time. Remote sensing has been widely used as a powerful means to conduct oil spill detection. Optical polarization remote sensing, thriving in recent years, shows a novel potential for oil spill detection. This paper provides a demonstration of the use of open-source POLDER/PARASOL polarization time-series data to detect oil spill. The Deepwater Horizon oil spill, one of the largest oil spill disasters, is utilized to explore the potential of optical polarization remote sensing for oil spill detection. A total of 24 feature combinations are organized to quantitatively study the positive effect of adding polarization information and the appropriate way to describe polarization characteristics. Random forest classifier models are trained with different combinations, and the results are assessed by 10-fold cross-validation. The improvement from adding polarization characteristics is remarkable ((average) accuracy: +0.51%; recall: +2.83%; precision: +3.49%; F₁ score: +3.01%, (maximum) accuracy: +0.80%; recall: +5.09%; precision: +6.92%; F₁ score: +4.72%), and coupling between the degree of polarization and the phase angle of polarization provides the best description of polarization information. This study confirms the potential of optical polarization remote sensing for oil spill detection, and some detailed problems related to model establishment and polarization feature characterization are discussed for the further application of polarization information.

Visual tracking methods have been successful in recent years. Correlation filter (CF)-based methods significantly advanced state-of-the-art tracking. The advancement in CF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, there would be trouble if the tracker indiscriminately learned samples. Particularly, when the target is severely occluded or out-of-view, the tracker will continuously learn the wrong information, resulting target loss in the following frames. In this study, aiming to avoid incorrect training when occlusions occur, we propose a regional color histogram-based occlusion estimating agency (RCHBOEA), which estimates the occlusion level and then instructs, based on the result, the tracker to work in one of two modes: normal or lost. In normal mode, an occlusion level-based self-adopting learning rate is used for tracker training. In lost mode, the tracker pauses its training and conducts a search and recapture strategy on a wider searching area. Our method can easily complement CF-based trackers. In our experiments, we employed four CF-based trackers as a baseline: discriminative CFs (DCF), kernelized CFs (KCF), background-aware CFs (BACF), and efficient convolution operators for tracking: hand-crafted feature version (ECO_HC). We performed extensive experiments on the standard benchmarks: VIVID, OTB50, and OTB100. The results demonstrated that combined with RCHBOEA, the trackers achieved a remarkable improvement.

Polarization is an independent dimension of light wave information that has broad application prospects in machine vision and remote sensing tasks. Polarization imaging using a division-of-focal-plane (DoFP) polarimetric sensor can meet lightweight and real-time application requirements. Similar to Bayer filter-based color imaging, demosaicing is a basic and important processing step in DoFP polarization imaging. Due to the differences in the physical properties of polarization and the color of light waves, the widely studied color demosaicing method cannot be directly applied to polarization demosaicing. We propose a polarized intensity ratio constraint demosaicing model to efficiently account for the characteristics of polarization detection in this work. First, we discuss the special constraint relationship between the polarization channels. It can be simply described as: for a beam of light, the sum of the intensities detected by any two vertical ideal analyzers should be equal to the total light intensity. Then, based on this constraint relationship and drawing on the concept of guided filtering, a new polarization demosaicing method is developed. A method to directly use raw images captured by the DoFP detector as the ground truth for comparison experiments is then constructed to aid in the convenient collection of experimental data and extensive image scenarios. Results of both qualitative and quantitative experiments illustrate that our method is an effective and practical method to faithfully recover the full polarization information of each pixel from a single mosaic input image.

Since mid-March 2020, global COVID-19 pandemic has experienced an exponential growth in process from sporadic to sudden outbreaks. This paper selects the 8-day surge data of daily cases, death and recovery rates (March 19-26, 2020) from 18 countries with severe pandemic situation to discuss the impact of 9 factors of both socioeconomic and natural on the pathogen outbreak. Moreover, the paper also elaborates analysis and comparison of relatively slow 4-week (February 1-29, 2020) data of China’s surge cases to determine the relationship between social and natural factors and on the spread of pandemic, which provides an effective reference for delaying and controlling the pandemic development.