5 February 2004 ARMA-model-based region-growing method for extracting lake region in a remote sensing image
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Abstract
Recently the lake area detection has been a popular topic for time series remote sensing images analysis. The two-dimensional Markov model is one of the efficient mathematical models to describe an image especially when the within-object interpixel correlation varies significantly from object to object. The unsupervised Region Growing is a powerful image segmentation method for use in shape classification and analysis. In this paper, the Region Growing method based on two-dimensional Autoregressive Moving Average (ARMA) model is proposed for lake region detections. Some of the statistical techniques, such as Gaussian distributed white noise error confidence interval, and sample statistics based on mean and variance properties have been used for thresholding during calculations. The linear regression analysis with least mean squares estimation is still of ongoing interest for statistical research and applications especially with the remote sensing images. The LANDSAT 5 database in the area of Italy's Lake Mulargias acquired in July 1996 was used for the computing experiments with satisfactory preliminary results.
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Chi Hau Chen, Peter Pei-Gee Ho, "ARMA-model-based region-growing method for extracting lake region in a remote sensing image", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.510496; https://doi.org/10.1117/12.510496
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