Unsupervised Wavelet-Feature Markov Clustering Algorithm for Remotely Sensed Images

Wavelet-feature Markov clustering algorithm for the remotely sensed data is based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying wavelet transform on the expanded multispectral data. The correlation ratio between the two samples is a statistical calculation of the matched peak point positions on the wavelet-feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. By applying a simulated annealing method and gradually shrunk clustering size, Markov clustering leads to the best class centers quickly at each clustering temperature. The experimental results about TM data have verified its acceptable clustering accuracy and high convergence velocity.