In China, visibility condition has become an important issue that concerns both society and the scientific community. In order to study visibility characteristics and its influencing factors, visibility data, air pollutants, and meteorological data during the year 2013 were obtained over Shanghai. The temporal variation of atmospheric visibility was analyzed. The mean value of daily visibility of Shanghai was 19.1 km. Visibility exhibited an obvious seasonal cycle. The maximum and minimum visibility occurred in September and December with the values of 27.5 and 7.7 km, respectively. The relationships between the visibility and air pollutant data were calculated. The visibility had negative correlation with NO2, CO, PM2.5, PM10, and SO2 and weak positive correlation with O3. Meteorological data were clustered into four groups to reveal the joint contribution of meteorological variables to the daily average visibility. Usually, under the meteorological condition of high temperature and wind speed, the visibility of Shanghai reached about 25 km, while visibility decreased to 16 km under the weather type of low wind speed and temperature and high relative humid. Principle component analysis was also applied to identify the main cause of visibility variance. The results showed that the low visibility over Shanghai was mainly due to the high air pollution concentrations associated with low wind speed, which explained the total variance of 44.99 %. These results provide new knowledge for better understanding the variations of visibility and have direct implications to supply sound policy on visibility improvement in Shanghai.

Aiming at the sensitivity of fuzzy C-means (FCM) method to the initial clustering center and noise data, and the single feature being not able to segment the image effectively, this paper proposes a new image segmentation method based on fuzzy clustering with cellular automata (CA) and features weighting. Taking the gray level as the object and combining fully the image feature and the spatial feature weighting and FCM, this paper quickly realizes the fuzzy clustering of the images segmentation by the CA's self-iteration function and finally discusses the effectiveness and feasibility of the proposed method in long-term sequences satellite remote sensing image segmentation. Our experiments show that the proposed method not only has fast convergence speed, strong anti-noise property, and robustness, but also can effectively segment common images and long-term sequence satellite remote sensing images and has good applicability.

The efficient classification of remote sensing images (RSIs) has become the key of remote sensing application. To tackle the high computational cost in the traditional classification method, in this paper we propose a new RSI classification method based on improved convolutional neural network (CNN) and support vector machine (SVM) (CNN-SVM). In this method, we first designed a seven-layer CNN structure and took the ReLU function as the activation function. We then inputted the RSI into the CNN model and extracted feature maps and replaced the output layer of the CNN network via training the feature maps in the SVM classifier. Next, taking the simulation experiments of MNIST handwritten digital dataset and UC Merced Land Use remote sensing dataset as examples, we tested and verified the proposed method in this experiment. Finally, the empirical study of volcanic ash cloud (VAC) classification from moderate resolution imaging spectroradiometer (MODIS) RSI was carried out and evaluated. The experimental results show that compared with the traditional methods, the proposed method has lower loss value and better generalization in modeling training; the total classification accuracy of VAC and Kappa coefficient reached 93.5% and 0.8502, respectively, and achieved preferable VAC identification and visual effects. It will enhance the classification accuracy to the massive remote sensing data.