Unsupervised segmentation of ERS and Radarsat sea ice images using multiresolution peak detection and aggregated population equalization
In this paper we describe Automated Sea Ice Segmentation (ASIS), a system that automatically segments Synthetic Aperture Radar (SAR) sea ice imagery. This system integrates image processing, data mining, and machine learning methodologies to determine the number of visually separable classes in ERS and Radarsat sea ice images. We introduce two new techniques: multiresolution peak detection and spatial clustering. The detection is a noise-resistant data discretization methodology that results in an initial segmentation of the image. The clustering is based on an innovative concept called Aggregated Population Equalization that utilizes spatial relationships among classes to merge and split the population environment. Its self-organizing ability produces the final segmentation and automates ASIS. In addition, we have designed a Java-based graphical user interface that facilitates post-segmentation human evaluation and classification. Thus, ASIS can be used as a pre-processor to help analyse sea ice images as well as a basis for human classification of sea ice images. We have tested the system on more than 300 ERS-1, ERS-2 and Radarsat SAR sea ice images and analysed the results to point out the strengths and weaknesses of ASIS in the automated segmentation of sea ice images.