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Abstract-The objective of this paper is to analyze the behavior of specular scattering for different soil texture fields at various soil moisture (m v ) and analyze the data to retrieve the soil moisture with minimizing the effect of the soil texture. To study the soil texture effect on specular scattering 10 different soil fields were prepared on the basis of change in soil constituents (i.e., percentage of sand, silt and clay) and experiments were performed in both like polarizations (i.e., HH-polarization and V V -polarization) at various incidence angles (i.e., varying incidence angle from 25 • to 70 • in step of 5 • ). Angular response of specular scattering coefficients (σ • hh in HH-polarization and σ • vv in V V -polarization) were analyzed for different soil texture fields with varying soil moisture content whereas the surface roughness condition for all the observations were kept constant. The changes in specular scattering coefficient values were observed with the change in soil texture fields with moisture for both like polarizations. Further, copolarization ratio (P = σ • hh /σ • vv ) study was performed and it was observed that the dependency of copolarization ratio for change in soil texture field at constant soil moisture is less prominent whereas the value of copolarization ratio is varying with variation of moisture content. This emphasizes that copolarization ratio may be minimizing the effect of soil texture while observing the soil moisture on specular direction. Regression analysis is carried out to select the best suitable incidence angle for observing the moisture and texture at C-band in specular direction and 60 • incidence angle was found the best suitable incidence angle. An empirical relationship between P and m v was developed for the retrieval of m v and the obtained relationship gives a good agreement with observed m v . In addition, m v was also retrieved

This paper proposes a fully automated method for MR brain image segmentation into Gray Matter, White Matter and Cerebro-spinal Fluid. It is an extension of Fuzzy C Means Clustering Algorithm which overcomes its drawbacks, of sensitivity to noise and inhomogeneity. In the conventional FCM, the membership function is computed based on the Euclidean distance between the pixel and the cluster center. It does not take into consideration the spatial correlation among the neighboring pixels. This means that the membership values of adjacent pixels belonging to the same cluster may not have the same range of membership value due to the contamination of noise and hence misclassified. Hence, in the proposed method, the membership function is convolved with mean filter and thus the local spatial information is incorporated in the clustering process. The method further includes pixel re-labeling and contrast enhancement using non-linear mapping to improve the segmentation accuracy. The proposed method is applied to both simulated and real T1-weighted MR brain images from BrainWeb and IBSR database. Experiments show that there is an increase in segmentation accuracy of around 30% over the conventional methods and 6% over the state of the art methods.

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The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.