In this study, we present an unsupervised change detection method using multi-spectral and multi-temporal remotely sensed imageries. This method is a pre-classification approach based on a spectral rule-based per-pixel classifier (SRC) developed by Baraldi et al. (2006). SRC is purely based on spectral-domain prior knowledge, such that no training or supervision process is needed. To explore its capability to detect change, we applied it in the Zhoushan Islands, Zhejiang, China. First, images were classified by SRC, and change detection was performed by two separate methods. One was the comparison of the merged categories obtained by reclassifying the pre-classification types of SRC. The other was comparing bi-temporal pre-classification types directly. The classification accuracy of the merged categories based on SRC was compared to the Maximum Likelihood Classifier and Support Vector Machine. The accuracy of the change detection was assessed and compared to results processed by the common post-classification comparison and change vector analysis methods. Results show that the change detection by directly comparing pre-classification types of SRC had the highest accuracy (overall accuracy was 90%, kappa coefficient was 0.81) among these methods and that the method of comparing merged categories was the worst (overall accuracy was 73%, kappa coefficient was only 0.46).
Aerosol optical thickness (AOT) and atmospheric visibility are two important weather parameters. AOT reflects the state of the atmosphere,-and atmospheric visibility is widely used in various aspects of social life. Generally, it is reported in literatures that both of them are affected by Air Pollutants and other meteorological factors, such as surface pressure, ground temperature, wind speed, precipitation. In this paper, a statistic relationship expression is established between AOT and atmospheric visibility on the basis of the point-to-point meteorological observations. In the national region, the correlation between atmospheric visibility and weather factors indicates that the surface pressure has great influence on atmospheric visibility all the year round. And the influence based on precipitation is more obvious in spring and summer, mean-while wind speed and temperature play important roles in autumn and winter. A significant positive correlation was found between AOT and API. To express the relationship between atmospheric visibility and AOT, some computable models were utilized. According to the accuracy analysis, the cubic curve model and the power function model are more accurate. And both RMSE (root-mean-square error) of them is higher than 0.47. But the coefficient of cubic curve is more complex in practice. Finally, a simple estimation model of aerosol optical thickness based on meteorological station observed atmospheric visibility was conducted using power function. The Pearson coefficient between calculation of power function and observation is 0.73.
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