Unsupervised Wavelet-Feature Correlation Ratio Markov Clustering Algorithm for Remotely Sensed Images

Abstract

The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. The wavelet-feature correlation ratio Markov clustering algorithm (WFCRMCA) for remotely sensed data is proposed 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 a wavelet transform to spectral data. The correlation ratio between 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 the computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. Markov clustering applies several strategies, such as a simulated annealing method and gradually shrinking the clustering size, to control the clustering convergence. It can quickly obtain the best class centers at each clustering temperature. The experimental results of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Thermal Mapping (TM) data have verified its acceptable clustering accuracy and high convergence velocity.

Keywords

• hyper-spectral images
• wavelet
• simulated annealing
• Markov clustering