A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.

Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%.

Characterization of particulate products for aging of ethylbenzene secondary organic aerosol in the presence of ammonium sulfate seed aerosol

Aging of secondary organic aerosol（SOA） particles formed from OH– initiated oxidation of ethylbenzene in the presence of high mass（100–300 μg/m^3） concentrations of（NH_4）_2SO_4seed aerosol was investigated in a home-made smog chamber in this study.The chemical composition of aged ethylbenzene SOA particles was measured using an aerosol laser time-of-flight mass spectrometer（ALTOFMS） coupled with a Fuzzy C-Means（FCM） clustering algorithm.Experimental results showed that nitrophenol,ethyl-nitrophenol,2,4-dinitrophenol,methyl glyoxylic acid,5-ethyl-6-oxo-2,4-hexadienoic acid,2-ethyl-2,4-hexadiendioic acid,2,3-dihydroxy-5-ethyl-6-oxo-4-hexenoic acid,1H-imidazole,hydrated N-glyoxal substituted1H-imidazole,hydrated glyoxal dimer substituted imidazole,1H-imidazole-2-carbaldehyde,N-glyoxal substituted hydrated 1H-imidazole-2-carbaldehyde and high-molecular-weight（HMW） components were the predominant products in the aged particles.Compared to the previous aromatic SOA aging studies,imidazole compounds,which can absorb solar radiation effectively,were newly detected in aged ethylbenzene SOA in the presence of high concentrations of（NH_4）_2SO_4seed aerosol.These findings provide new information for discussing aromatic SOA aging mechanisms.