Automated Glacier Segmentation by Fast Adaptive Medoid Shift Algorithm

An optimal approach to unsupervised colour image clustering is presented in this research which is suited for high resolution images based on mode seeking by mediod shifts. It is shown that automatic detection of total number of clusters depends upon overall image statistics as well as the bandwidth of the underlying probability density function. An optimized adaptive mode seeking algorithm based on reverse parallel tree traversal is proposed. This work has contribution in three aspects. 1) Adaptive bandwidth for kernel function is proposed; 2) A novel reverse parallel tree traversing approach for mode seeking is presented; 3) For high resolution images block clustering based optimized Adaptive Mediod Shift (AMS) is proposed. The proposed method has made it possible to perform clustering on variety of high resolution images. The proposed method is applied to automated glacier segmentation from 6 band Landsat TM sensor satellite images of Hindukush and KaraKoram mountain ranges of Asia. The method along with other proposed techniques successfully delineates the glaciers into class Clean Glacier and class Debris Covered Glacier.