426 research outputs found
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations
The interactions between PM2.5 and meteorological factors play a crucial role
in air pollution analysis. However, previous studies that have researched the
relationships between PM2.5 concentration and meteorological conditions have
been mainly confined to a certain city or district, and the correlation over
the whole of China remains unclear. Whether or not spatial and seasonal
variations exit deserves further research. In this study, the relationships
between PM2.5 concentration and meteorological factors were investigated in 74
major cities in China for a continuous period of 22 months from February 2013
to November 2014, at season, year, city, and regional scales, and the spatial
and seasonal variations were analyzed. The meteorological factors were relative
humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS).
We found that spatial and seasonal variations of their relationships with PM2.5
do exist. Spatially, RH is positively correlated with PM2.5 concentration in
North China and Urumqi, but the relationship turns to negative in other areas
of China. WS is negatively correlated with PM2.5 everywhere expect for Hainan
Island. PS has a strong positive relationship with PM2.5 concentration in
Northeast China and Mid-south China, and in other areas the correlation is
weak. Seasonally, the positive correlation between PM2.5 concentration and RH
is stronger in winter and spring. TEM has a negative relationship with PM2.5 in
autumn and the opposite in winter. PS is more positively correlated with PM2.5
in autumn than in other seasons. Our study investigated the relationships
between PM2.5 and meteorological factors in terms of spatial and seasonal
variations, and the conclusions about the relationships between PM2.5 and
meteorological factors are more comprehensive and precise than before.Comment: 3 tables, 13 figure
- …