A New Fuzzy Clustering Method With Neighborhood Distance Constraint for Volcanic Ash Cloud

Remote sensing classification for volcanic ash cloud is a difficult task in the remote sensing application, and how to accurately obtain the volcanic ash cloud information from remote sensing image has become a key step in remote sensing classification of volcanic ash cloud. Aiming at the characteristics of the remote sensing images, via introducing the neighborhood pixels based on the classical fuzzy C-means clustering algorithm, this paper proposed a new fuzzy clustering remote sensing classification method with neighborhood distance constraint for volcanic ash cloud. This paper is tested from simulation texture image and moderate resolution imaging spectroradiometer remote sensing image, and finally explored the Sangeang Api volcanic ash cloud case on May 30, 2014. Our experiments show that the proposed method can effectively classify the volcanic ash cloud from remote sensing images, and the overall classification accuracy and Kappa coefficient reach 88.4% and 0.8064. To some extent, it overcomes the deficiency of the approaches in traditional volcanic ash cloud remote sensing classification.