UNDERSTANDING VERTICAL GROUND DISPLACEMENT FROM P-SBAS USING GETIS-ORD GI* STATISTIC

The Differential Synthetic Aperture Radar Interferometric (DInSAR) algorithm has already shown its importance in volcanicmonitoring. However, it is limited by atmospheric perturbations or temporal decorrelation, implying the existence of lowcoherencerecords that must be discarded. In vast studied areas, with thousands of points unevenly distributed, theinterpretation of the results is usually complicated. This text presents an analysis of the vertical component variation onTenerife island (Canary Islands), from 2005 to 2010, using Getis-Ord Gi* spatial statistic on ENVISAT DinSAR images.The ascending and descending images have been processed using the Parallel Small BAseline Subset (P-SBAS)algorithm, within ESA Grid Processing on Demand cloud environment. From Line of Sight results of both tracks, the verticaldeformation speeds have been calculated over 72,207 points with high coherence (> 0.7). Finally, the Gi* statistic hasbeen applied, obtaining a map with statistical significance, where the high values of Gi*, both positive and negative, implythe spatial clustering of likely ground movements. This map highlights areas with variable vertical kinematics on TenerifeIsland, contributing to understanding of its geodynamics. The displacements obtained coincide with previous studies, evenshowing possible new relationships between some phenomena that should be considered. The Gi* spatial statistic is anefficient and quick tool to extract information in a regional scale kinematic study.

Gridding and Segmentation Method for DNA Microarray Images

This article mainly explores meshing and segmentation techniques for microarray image analysis. The term "grid" refers to dividing an image into subgrids of dots and then dividing them into point detection. Most of the existing methods depend on input parameters such as the number of rows / columns, the number of points in each row / column, the size of the subarrays, etc. This article proposes a fully automatic mesh generation algorithm. This can remove any initialized parameter without any manual intervention. In the segmentation step, clustering algorithms are used because they do not consider the size and shape of the spots, do not depend on the initial state of the pixels, and do not require post-processing. In this article, a new method is proposed to estimate the initial parameters (centroid and number of clusters) required by any clustering algorithm. Qualitative and quantitative analysis shows that the algorithm can perform grid processing on microarray images well, and improves the performance of the clustering algorithm.

Performance Analysis of Image Processing in Hexagonal Grid Based on Image Compression, Denoising and Steganography

This paper gives a summary of different works we had performed in hexagonal grid for the performance analysis of image processing in hexagonal grid. Processing based on hexagonal grid is a new approach in image processing because of its various advantages. This paper covers image compression and image denoising in hexagonal grid. Image compression using wavelet transform is performed in hexagonal grid. The performance of compression in hexagonal grid and conventional rectangular grid are analyzed in terms of Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). It is observed that better PSNR is obtained in the case of hexagonal grid. Wavelet thresholding based method is done for image denoising in hexagonal grid. The thresholding method is named as ‘Jeevkrish’ method, which is the modification of NeighShrinkthresholding method. The performance is analyzed using MSE, PSNR and Structural Similarity (SSIM) Index. An algorithm (‘JK’ algorithm) for generating a pseudo hexagonal structure image and a method for image hiding using the pseudo hexagonal image as the cover image are also discussed in this paper

An algorithm for wavelet thresholding based image denoising by representing images in hexagonal lattice

The existing method of representation for digital images is using square shaped picture elements called pixels in a rectangular grid. Processing based on hexagonal grid is a new approach in image processing. It has various advantages like symmetry, higher angular resolution, consistent connectivity and higher sampling efficiency. Image processing applications like rotation, scaling, edge detection, and compression in hexagonal domain have already been discussed by many researchers. In this paper we propose an image denoising scheme in hexagonal lattice using wavelet thresholding method. For the thresholding of wavelet coefficients, modified NeighShrink thresholding method is applied. In NeighShrink method, sub-optimal universal threshold and identical neighboring window size in all wavelet sub-bands are used. However, in the proposed method, instead of sub-optimal universal threshold, an optimal threshold is determined for every wavelet sub-band by the Stein’s Unbiased Risk Estimate (SURE). Denoising is performed on images represented in rectangular grid as well as hexagonal grid using modified thresholding method for comparison. MSE, PSNR and SSIM are used for the performance analysis. The obtained results confirm that the proposed method gives better results than existing algorithms.

An algorithm for detecting abnormal electricity mode of power users

In order to reduce the non-technical loss and reduce the operating cost of the power company, an abnormal power consumption detection algorithm is proposed. The algorithm includes feature extraction, principal component analysis, grid processing, local outliers, and so on. Firstly, we extract several feature quantities that characterize the user's power consumption pattern, and map the X users to the two-dimensional plane by principal component analysis. Data visualization and easy to calculate local outliers, and grid processing techniques to filter out data points in low density regions. The algorithm is used to reduce the number of training samples in the power user data set, and to output the anomalies and probabilities of all users' behavior. The experimental results show that the use of the sorting only need to detect the anomaly of a few users can find a large number of abnormal users, significantly improve the efficiency of the algorithm.

Improved Statistical Algorithm in Digital Terrain

Neighbor statistic algorithm is a kind of commonly used algorithm, statistical analysis was carried out on certain analysis value through the grid in the window, to reflect the local and zone terrain feature. This paper discussed the method of parallel algorithm based on neighborhood statistics serial algorithm parses and terrain algorithm. Focus on the data partition strategy and aperture effects processing strategy. Experiments show that, the two optimization method proposed can make the parallel program neighborhood grid processing algorithms make full use of parallel computing resources, and then further enhance its parallel performance on the general parallelization.