SOM Based Segmentation Method to Identify Water Region in LANDSAT Images


  • Tiagrajah V. Janahiraman


LANDSAT satellite image, SOM neural network, water region segmentation


The objective of this research is to identify the water region from LANDSAT satellite image. Water resources are sources of water that are useful or potentially useful to humans. Uses of water include agriculture, industrial, household, recreational, transportation and environmental activities. Surveying of water region and research on its feature is very basic step for many planning, especially for countries like Indonesia, where the rapid economic growth has caused increasing
competition for water. Identifying water region from satellite images is one of the grand steps of water resources management for a country. In this paper, the segmentation algorithm based on SOM (self-organizing map) neural network with compression pre-processing by wavelet transform and image smoothing using Gaussian low-pass frequency domain filters is presented. Firstly, the input image is blurred using Gaussian low-pass frequency domain filter. Then wavelet decomposition is used for obtaining compressed image without affecting other features. Next, SOM neural network is trained with the approximation image, which can improve the representation of training. Finally, trained neural network classify pixels of original image by using K-mean algorithm.