Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network

The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution.The selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit,which will improve the timeliness of oil spill emergency monitoring.At the same time,the combination of spectral and spatial features can improve the accuracy of oil spill monitoring.Two ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions,for which the multiscale superpixel level group clustering framework(MSGCF)was used to select spectral feature bands with strong separability.In addition,the double-branch dual-attention(DBDA)model was applied to identify crude oil and its emulsions.Compared with the recognition results based on original hyperspectral images,using the feature bands determined by MSGCF improved the recognition accuracy,and greatly shortened the running time.Moreover,the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined,and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in 2010.This study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration,laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit.

Detection of Contamination Defect on Ice Cream Bar Based on Fuzzy Rule and Absolute Neighborhood

The contamination proposed in this paper is a defect on the surface of ice cream bar,which is a serious security threat.So it is essential to detect this defect before launched on the market. A detection method of contamination defect on the ice cream bar surface is proposed,which is based on fuzzy rule and absolute neighborhood feature. Firstly,the ice cream bar surface is divided into several sub-regions via the defined adjacent gray level clustering method. Then the alternative contamination regions are extracted from the sub-regions via the defined fuzzy rule. At last,the real contamination regions are recognized via the relationship between absolute neighborhood gray feature and default threshold. The algorithm was tested in the self-built image database SUT-D. The results show that the accuracy of the method proposed in this paper is 97.32 percent,which increases 2.68 percent at least comparing to the other typical algorithms. It indicates that the superiority proposed in this paper,which is of actual use value.

Genetic climatic regionalization of the Balkan Peninsula using cluster analysis

Sea Level Pressure（SLP） data for the period 1950–2012 at 61 stations located in or around the Balkan Peninsula was used. The main concept is that intra-annual course of SLP represents the best different air masses that are situated over the Balkan Peninsula during the year. The method for differentiation of climatic zones is cluster analysis. A hierarchical clustering technique–average linkage between groups with Pearson correlation for measurement of intervals was employed in the research. The climate of the Balkan Peninsula is transitional between oceanic and continental and also between subtropical and temperate climates. Several major changes in atmospheric circulation over the Balkan Peninsula have happened over the period 1950–2012. There is a serious increase of the influence of the Azores High in the period January–Marchwhich leads to an increase of SLP and enhances oceanic influence. There is an increase of the influence of the north-west extension of the monsoonal low in the period June–September. This leads to more continental climatebut also to more tropical air masses over the Balkan Peninsula. Accordinglythe extent of subtropical climate widens in northern direction. There is an increase of the influence of the Siberian High in the period October–December. This influence covers central and eastern part of the peninsula in October and Novemberand it reaches western parts in December. Thusthe climate becomes more continental.