Drift ice recognition using remote sensing data by neural networks
In recent years, observation of a wide variety in the Earth's surface can be done by improvement of remote sensing technology. The purpose of the paper is to recognize a drift ice as thick ice, thin ice, and sea using synthetic aperture radar (SAR) images. The recognition of the drift ice is achieved by using neural networks (NN). The neural network applies two methods, a BP trained neural network and a self-organizing map. Training data are image features extracted from SAR images. There are three methods for extracting the features: Fourier transform, high-order autocorrelation function (HACF), and image features based on a run length method. We carry out a comparative experiment, and demonstrate their effectiveness by means of computer simulation.