Synthetic Aperture Radar (SAR) is a special type of imaging radar that involves advanced technology and complex data processing to obtain detailed images from the lake surface. Lake ice typically reflects more of the radar energy emitted by the sensor than the surrounding area, which makes it easy to distinguish between the water and the ice surface. In this research work, SAR images are used for ice classification based on supervised and unsupervised classification algorithms. In the pre-processing stage, Hue saturation value (HSV) and Gram-Schmidt spectral sharpening techniques are applied for sharpening and resampling to attain highresolution pixel size. Based on the performance evaluation metrics it is proved that Gram-Schmidt spectral sharpening performs better than sharpening the HSV between the boundaries. In classification stage, Gram-Schmidt spectral technique based sharpened SAR images are used as the input for classifying using parallelepiped and ISO data classifier. The performances of the classifiers are evaluated with overall accuracy and kappa coefficient. From the experimental results, ice from water is classified more accurately in the parallelepiped supervised classification algorithm.

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Abstract-Identification of masked objects especially in detection of landmines is always a difficult problem due to environmental inference. Here, segmentation phase is highly concentrated by performing an initial spatial segmentation to achieve a minimal number of segmented regions while preserving the homogeneity criteria of each region. This paper aims in evaluating similarities based segmentation methods to compose the partition of objects in Infra-Red images. The output is a set of non-overlapping homogenous regions that compose the pixels of the image. These extracted regions are used as the initial data structure in feature extraction process. Experimental results conclude that h-maxima transformation provides better results for landmine detection by taking the advantage of the threshold. The relative performance of different conventional methods and proposed method are evaluated and compared using the Global Consistency Error and Structural Content. It proves that h-maxima gives significant results that definitely facilitate the landmine classification system more effectively.

SUMMARY :Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. Forty five different sampling locations in Sindapalli Uppodai were selected and undisturbed samples were taken to measure the water content at field capacity (FC), -33 kPa, and permanent wilting point (PWP), -1500 kPa. Measured soil variables included texture, organic carbon, water percentage at field capacity and wilting point, water saturation percentage, Bulk density were also determined for each soil sample at each location. Three different techniques including pattern recognition approach Artificial Neural Network (ANN), pedo transfer functions (PTF) and field measurement were used to predict the soil water at each sampling location. Root mean square error (RMSE), mean error (ME) and co-efficient of determination (R 2 ) were used to evaluate the performance of all the three approaches. Our results showed that field measurement and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between field measurement and PTF, the former, predicted water content at PWP more accurate than PTF; however, both approach showed a similar accuracy to predict water content at FC.How to cite this article : Balathandayutham, K., Valliammai, A. and Krishnaveni, M. (2017). Evaluation of artificial neural network and regression PTFs in estimation soil hydraulic properties.

Facial Expression Recognition (FER) is a prominent research area in Computer Vision and Artificial Intelligence that has been playing a crucial role in human–computer interaction. The existing FER system focuses on spatial features for identifying the emotion, which suffers when recognizing emotions from a dynamic sequence of facial expressions in real time. Deep learning techniques based on the fusion of convolutional neural networks (CNN) and long short-term memory (LSTM) are presented in this paper for recognizing emotion and identifying the relationship between the sequence of facial expressions. In this approach, a hyperparameter tweaked VGG-19 skeleton is employed to extract the spatial features automatically from a sequence of images, which avoids the shortcoming of the conventional feature extraction methods. Second, these features are given into bidirectional LSTM (Bi-LSTM) for extracting spatiotemporal features of time series in two directions, which recognize emotion from a sequence of expressions. The proposed method’s performance is evaluated using the CK+ benchmark as well as an in-house dataset captured from the designed IoT kit. Finally, this approach has been verified through hold-out cross-validation techniques. The proposed techniques show an accuracy of 0.92% on CK+, and 0.84% on the in-house dataset. The experimental results reveal that the proposed method outperforms compared to baseline methods and state-of-the-art approaches. Furthermore, precision, recall, F1-score, and ROC curve metrics have been used to evaluate the performance of the proposed system.