21 October 2014 Prediction of water quality parameters from SAR images by using multivariate and texture analysis models
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Abstract
Remote sensing is one of the most important tools for monitoring and assisting to estimate and predict Water Quality parameters (WQPs). The traditional methods used for monitoring pollutants are generally relied on optical images. In this paper, we present a new approach based on the Synthetic Aperture Radar (SAR) images which we used to map the region of interest and to estimate the WQPs. To achieve this estimation quality, the texture analysis is exploited to improve the regression models. These models are established and developed to estimate six common concerned water quality parameters from texture parameters extracted from Terra SAR-X data. In this purpose, the Gray Level Cooccurrence Matrix (GLCM) is used to estimate several regression models using six texture parameters such as contrast, correlation, energy, homogeneity, entropy and variance. For each predicted model, an accuracy value is computed from the probability value given by the regression analysis model of each parameter. In order to validate our approach, we have used tow dataset of water region for training and test process. To evaluate and validate the proposed model, we applied it on the training set. In the last stage, we used the fuzzy K-means clustering to generalize the water quality estimation on the whole of water region extracted from segmented Terra SAR-X image. Also, the obtained results showed that there are a good statistical correlation between the in situ water quality and Terra SAR-X data, and also demonstrated that the characteristics obtained by texture analysis are able to monitor and predicate the distribution of WQPs in large rivers with high accuracy.
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Muntadher A. Shareef, Muntadher A. Shareef, Abdelmalek Toumi, Abdelmalek Toumi, Ali Khenchaf, Ali Khenchaf, } "Prediction of water quality parameters from SAR images by using multivariate and texture analysis models", Proc. SPIE 9243, SAR Image Analysis, Modeling, and Techniques XIV, 924319 (21 October 2014); doi: 10.1117/12.2067262; https://doi.org/10.1117/12.2067262
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