Automatic road extraction using high resolution satellite images based on Level Set and Mean Shift methods

Analysis of high resolution satellite images has been an important research topic for urban analysis. One of the important features of urban areas in urban analysis is the automatic road network extraction. Two approaches for road extraction based on Level Set and Mean Shift methods are proposed. From an original image it is difficult and computationally expensive to extract roads due to presences of other road-like features with straight edges. The image is preprocessed to improve the tolerance by reducing the noise (the buildings, parking lots, vegetation regions and other open spaces) and roads are first extracted as elongated regions, nonlinear noise segments are removed using a median filter (based on the fact that road networks constitute large number of small linear structures). Then road extraction is performed using Level Set and Mean Shift method. Finally the accuracy for the road extracted images is evaluated based on quality measures. The 1m resolution IKONOS data has been used for the experiment.

Multi-objective optimization of satellite image registration using discrete Pparticle swarm optimisation

A new multi-sensor image registration technique is proposed based on detecting the feature corner points using modified Harris Corner Detector (HDC). These feature points are matched using multi-objective optimization (distance condition and angle criterion) based on Discrete Particle Swarm Optimization (DPSO). This optimization process is more efficient as it considers both the distance and angle criteria to incorporate multi-objective switching in the fitness function. This optimization process helps in picking up three corresponding corner points detected in the sensed and base image and thereby using the affine transformation, the sensed image is aligned with the base image. Further, the results show that the new approach can provide a new dimension in solving multi-sensor image registration problems. From the obtained results, the performance of image registration is evaluated and is concluded that the proposed approach is efficient.

Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

Self catalytic growth of SnO2 branched nanowires by thermal evaporation

For the first time, Tin oxide (SnO2) multiple branched nanowires (NWs) have been synthesized by thermal evaporation of tin (Sn) in presence of oxygen without use of metal catalysts at low substrate temperature of 500 degrees C. Synthesized product consists of multiple branched nanowires and were single crystalline in nature. Each of the nanowire capped with catalyst particle at their ends. Energy dispersive X-ray analysis on the nanowires and capped nanoparticle confirms that Sn act as catalyst for SnO2 nanowires growth. A self catalytic vapor-liquid-solid (VLS) growth mechanism was proposed to describe the SnO2 nanowires growth. (C) 2012 Elsevier B.V. All rights reserved.

Study of the effect of alkali mixture on V-O bond length in Oxyfluoro Vanadate glasses using Raman spectroscopy

Raman spectroscopic study on Oxyfluoro Vanadate glasses containing various proportions of lithium fluoride and rubidium fluoride was carried out to see an effect of mixture of alkali on vanadium-oxygen (V-O) bond length. Glasses with a general formula 40V(2)O(5) - 30BaF(2) - (30 - x) LiF - xRbF (x = 0-30) were prepared. Room temperature Raman spectra of these glass samples were recorded in back scattering geometry. The data presented is in ``reduced Raman intensity'' form with maximum peak scaled to 100. We have used v = Aexp(BR), where A and B are fitting parameters, to correlate the bond length R with Raman scattering frequency v. We observed that variation in bond length and its distribution about a most probable value can be correlated to the alkali environment present in these glasses. We also observed that all rubidium environment around the network forming unit is more homogenous than all lithium environment.

Crop stage classification of hyperspectral data using unsupervised techniques

The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.

Implementation study of relay selection schemes for energy harvesting WSNs

We propose energy harvesting technologies and cooperative relaying techniques to power the devices and improve reliability. We propose schemes to (a) maximize the packet reception ratio (PRR) by cooperation and (b) minimize the average packet delay (APD) by cooperation amongst nodes. Our key result and insight from the testbed implementation is about total data transmitted by each relay. A greedy policy that relays more data under a good harvesting condition turns out to be a sub optimal policy. This is because, energy replenishment is a slow process. The optimal scheme offers a low APD and also improves PRR.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Multiobjective discrete particle swarm optimization for multisensor image alignment

A new technique is proposed for multisensor image registration by matching the features using discrete particle swarm optimization (DPSO). The feature points are first extracted from the reference and sensed image using improved Harris corner detector available in the literature. From the extracted corner points, DPSO finds the three corresponding points in the sensed and reference images using multiobjective optimization of distance and angle conditions through objective switching technique. By this, the global best matched points are obtained which are used to evaluate the affine transformation for the sensed image. The performance of the image registration is evaluated and concluded that the proposed approach is efficient.

Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

Study on important diffusion parameters of binary Ni3Sn4 phase

Important diffusion parameters, such as-parabolic growth constant, integrated diffusivity, ratio of intrinsic diffusivities of species Ni and Sn, Kirkendall marker velocity and the activation energy for diffusion kinetics of binary Ni3Sn4 phase have been investigated with the help of incremental diffusion couple technique (Sn/Ni0.57Sn0.43) in the temperature range 200-150 degrees C. Low activation energy extracted from Arrhenius plot indicates grain boundary controlled diffusion process. The species Sn is three times faster than Ni at 200 degrees C. Further, the activation energy of Sn tracer diffusivity is greater than that of Ni.

Novel co-evaporation approach for the growth of Sb doped SnO2 nanowires

Antimony doped tin oxide (Sb:SnO2) nanowires were grown by thermal and e-beam assisted co-evaporation of Sb and Sn in the presence of oxygen at a low substrate temperature of 450 degrees C. The field emission scanning electron microscopy study revealed that the nanowires had a length and diameter of 2-4 mu m and 20-60 nm respectively. Transmission electron microscopy study revealed the single crystalline nature of the nanowires; energy dispersive X-ray spectroscopy (EDS) and EDS mapping on the nanowires confirmed the presence of Sb doping in the nanowires. UV light detection study on the doped SnO2 nanowire films exhibited fast response and recovery time compared to undoped SnO2 nanowire films. This is an innovative and simple method to grow doped SnO2 nanowires.

Nucleosynthesis in the outflows associated with accretion disks of type II collapsars

We investigate nucleosynthesis inside the outflows from gamma-ray burst (GRB) accretion disks formed by the Type II collapsars. In these collapsars, massive stars undergo core collapse to form a proto-neutron star initially, and a mild supernova (SN) explosion is driven. The SN ejecta lack momentum, and subsequently this newly formed neutron star gets transformed to a stellar mass black hole via massive fallback. The hydrodynamics and the nucleosynthesis in these accretion disks have been studied extensively in the past. Several heavy elements are synthesized in the disk, and much of these heavy elements are ejected from the disk via winds and outflows. We study nucleosynthesis in the outflows launched from these disks by using an adiabatic, spherically expanding outflow model, to understand which of these elements thus synthesized in the disk survive in the outflow. While studying this, we find that many new elements like isotopes of titanium, copper, zinc, etc., are present in the outflows. Ni-56 is abundantly synthesized in most of the cases in the outflow, which implies that the outflows from these disks in a majority of cases will lead to an observable SN explosion. It is mainly present when outflow is considered from the He-rich, Ni-56/Fe-54-rich zones of the disks. However, outflow from the Si-rich zone of the disk remains rich in silicon. Although emission lines of many of these heavy elements have been observed in the X-ray afterglows of several GRBs by Chandra, BeppoSAX, XMM-Newton, etc., Swift seems to have not yet detected these lines.

Design and development of Fiber Bragg Grating sensing plate for plantar strain measurement and postural stability analysis

This paper describes an ab initio design and development of a novel Fiber Bragg Grating (FBG) sensor based strain sensing plate for the measurement of plantar strain distribution in human foot. The primary aim of this work is to study the feasibility of usage of FBG sensors in the measurement of plantar strain in the foot; in particular, to spatially resolve the strain distribution in the foot at different regions such as fore-foot, mid-foot and hind-foot. This study also provides a method to quantify and compare relative postural stability of different subjects under test; in addition, traditional accelerometers have been used to record the movements of center of gravity (second lumbar vertebra) of the subject and the results obtained have been compared against the outcome of the postural stability studies undertaken using the developed FBG plantar strain sensing plate. (C) 2013 Elsevier Ltd. All rights reserved.

Enhanced electrochemical performance of graphene nanosheet thin film anode decorated with tin nanoparticles

Graphene nanosheet (GNS) was synthesized by using microwave plasma enhanced CVD on copper substrate and followed by evaporation of tin metal. Scanning and transmission electron microscopy show that nanosize Sn particles are well embedded into the GNS matrix. The composition, structure, and electrochemical properties were characterized by X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), cyclic voltammetry (CV) and chrono-potentiometry. The first discharge capacity of as-deposited and annealed SnGNS obtained was 1551 mA h/g and 975 mA h/g, respectively. The anodes show excellent cyclic performance and coulombic efficiency.