PSO-Based Optimal Selection of Zernike Moments for Target Discrimination in High-Resolution SAR Imagery

Target discrimination is the key step of automatic target detection in synthetic aperture radar (SAR) images. In this paper, a new algorithm, effective and robust feature sets for target discrimination in high resolution SAR images has been proposed. Two main steps in target discrimination of SAR images have been developed, the feature extraction based on Zernike moments (ZMs) having linear transformation invariance properties and the PSO based feature selection to select the optimal feature subset of Zernike moments for decreasing computational complexity of feature extraction step. The input regions of interest (ROIs) have been segmented and passed to a number of preprocessing stages such as histogram equalization, position and size normalization. Two groups of Zernike moments (shape and margin (intensity) characteristic) have been extracted from the preprocessed images and they have been applied to the feature selection step. Each group includes 34 moments with different orders and iterations. The selected moments have been applied to a SVM classifier. The proposed scheme has been tested on the MSTAR database. The Receiver Operational Characteristics (ROC) curve and the performance of proposed method using some measured data have been analyzed. Experimental results demonstrate the efficiency of the proposed approach in target discrimination of SAR imagery.     

White spot syndrome virus inactivation study by using gamma irradiation

The present study was conducted to investigate the effect of gamma irradiation on white spot syndrome virus(WSSV). White spot syndrome virus is a pathogen of major economic importance in cultured penaeid shrimp industries. White spot disease can cause mortalities reaching 100% within 3–10 days of gross signs appearing. During the period of culture, immunostimulant agents and vaccines may provide potential methods to protect shrimps from opportunistic and pathogenic microrganisms. In this study, firstly, WSSV was isolated from infected shrimp and then multiplied in crayfish. WSSV was purified from the infected crayfish haemolymph by sucrose gradient and confirmed by transmission electron microscopy. In vivo virus titration was performed in shrimp, Penaeus semisulcatus. The LD50 of live virus stock was calculated 10 5.4 /mL. Shrimp post-larvae(1–2 g) were treated with gamma-irradiated(different doses) WSSV(10 0 to 10-4 dilutions) for a period of 10 days. The dose/survival curve for irradiated and un-irradiated WSSV was drawn; the optimum dose range for inactivation of WSSV and unaltered antigenicity was obtained 14–15 kGy. This preliminary information suggests that shrimp appear to benefit from treatment with gammairradiated WSSV especially at 14–15 KGy.     

Image Segmentation for Hydrothermal Alteration Mapping Using PCA and Concentration–Area Fractal Model

Information extraction from processed remotely sensed images, in the case of missing initial spectra of pixels, can be a challenge for the users. In such situations, application of conventional methods based on spectral properties of pixels is impractical. We took advantage of the fractal theory for image segmentation of a principal component (PC) image for hydrothermal alteration mapping. The selected input images included short wave infrared bands of ASTER imagery covering the Darrehzar porphyry copper mine and surrounding areas with well-known hydrothermal alteration zones. Hydrothermal alteration like other geological processes can show spatial distribution with fractal properties. Principal component analysis was used to enhance hydrothermal alteration associated with the Darrehzar porphyry copper deposit. None of the resulting PCs were appropriate to portray clearly important alteration types in the study area. The PC1 image, which contains more than 98% of variance of the input bands, was selected for image segmentation using a digital number–area technique based on the established concentration–area fractal model. This technique was proposed based on frequency distributions and spatial correlation and variability of pixel values. The resulting hydrothermal alteration map indicates intense phyllic, weak phyllic, and propylitic as the main alteration types exposed at the surface of the Darrehzar area. In addition, the proposed technique delineated the phyllic zone in the exposed mine pit and a transition zone between inner phyllic and surrounding propylitic alteration zones. Field investigation and sampling in 23 locations including spectral measurements, XRD and thin section studies, confirmed the accuracy of the classified image by the proposed technique.     

A comparison of the estimated effective elastic thickness of the lithosphere using terrestrial and satellite-derived data in Iran

The effective elastic thickness of the lithosphere has an important role in constraining compositional structure, geothermal gradient and tectonic forces within the lithosphere and the thickness of this layer can be used to evaluate the earthquakes’ focal depth. Hence, assessment of the elastic thickness of the lithosphere by gravitational admittance method in Iran is the main objective of this paper. Although the global geopotential models estimated from the satellite missions and surface data can portray the Earth’s gravity field in high precision and resolution, there are some debates about using them for lithosphere investigations. We used both the terrestrial data which have been provided by NCC (National Cartographic Center of Iran) and BGI (Bureau Gravimetrique International), and the satellite-derived gravity and topography which are generated by EIGEN-GL04C and ETOPO5, respectively. Finally, it is concluded that signal content of the satellite-derived data is as rich as the terrestrial one and it can be used for the determination of the lithosphere bending.     

Comparison of Different Methods for a Moho Modeling Under Oceans and Marginal Seas: A Case Study for the Indian Ocean

Surveys in Geophysics - Since marine seismic studies are relatively sparse and unevenly distributed, detailed tomographic images of the Moho geometry under large parts of the world’s oceans...     

Application of third generation shallow water wave models in a tidal environment

Wave modeling was performed in the German Bight of the North Sea during November 2002, using the spectral wave models, namely the K-model and Simulating WAves Nearshore (SWAN), both developed for applications in environments of shallow water depths. These models mainly differ with respect to their dissipation source term expressions and in exclusion or inclusion of nonlinear wave–wave interactions. The K-model uses nonlinear dissipation and bottom dissipation, and neglects quadruplet wave–wave interaction whereas, SWAN includes, besides bottom dissipation, dissipation by white-capping and depth induced wave breaking and triad wave–wave interaction. The boundary spectra were extracted from the WAM model results of a North Sea hindcast of the HIPOCAS project, wind fields, tidal current and water level variations from the results of models used in the Belawatt project. The purpose of this study was to test the performance of both wave models to see whether they were able to predict near-shore wave conditions accurately. The runs were performed with and without tidal current and level variations to determine their effect on the waves. Comparisons of model results with buoy measurements show that taking into account tides and currents improve the spectral shape especially in areas of high current speeds. Whereas SWAN performed better in terms of spectral shape, especially in case of two peaked spectra, the K-model showed better results in terms of integrated parameters.Responsible Editor: Hans Burchard     

Combining Group Method of Data Handling with Signal Processing Approaches to Improve Accuracy of Groundwater Level Modeling

Natural Resources Research - Groundwater level forecasting is a paramount necessity for integrated management of a basin. Development of suitable models is an essential step in determining...     

Independent Base Vector Representation to Address Endmember Variability in Hyperspectral Unmixing

The normal compositional model (NCM) is a well-known and powerful model in hyperspectral unmixing which represents endmembers as independent Gaussian vectors to capture endmember variability. However, the assumption of independent endmembers diminishes the model accuracy because the high degree of correlation between endmembers of a scene and identical sources of variability demonstrate that the endmembers are dependent. This paper proposes a new hyperspectral unmixing algorithm which represents endmembers using dependent Gaussian vectors to estimate abundance fractions. To overcome the higher complexity caused by dependence assumption, this algorithm introduces new independent Gaussian vectors named Base Vectors to represent different endmembers by a weighted linear combination. Also, the proposed unmixing algorithm uses maximum likelihood method to estimate weight coefficients of Base Vectors which are used to represent mixed pixel. Finally, abundance estimation can be done using the new representation for endmembers and mixed pixel. The proposed algorithm is evaluated and compared with other state-of-the-art unmixing algorithms using simulated and real hyperspectral images. Experimental results demonstrate that the proposed unmixing algorithm can unmix pixels composed of correlated endmembers in hyperspectral images in the presence of spectral variability more accurately than previous methods.     

Improved Minimum Miscibility Pressure Prediction for Gas Injection Process in Petroleum Reservoir

Determination of gas–oil minimum miscibility conditions is one of the important design parameters to improve the displacement efficiency of the hydrocarbon reservoir during enhanced oil recovery with gas injection. In this work, a support vector regression (SVR) model is developed using experimental data to estimate the minimum miscibility pressure (MMP) for various reservoir fluids and injection gases. Experimental MMP data taken from the reliable literature were used as input. Each data point input includes methane and intermediate components mole percent, plus fraction properties and reservoir temperature related to reservoir fluid and CO₂, H₂S, N₂ and intermediate mole fractions, and intermediate properties of the injected gas. Experimental MMP is regarded as the model output. The database contains 135 datasets, from which 125 datasets were used for model development, and the rest were used for model evaluation. Genetic algorithm was implemented to optimize the SVR model parameters. The proposed data-driven model was verified by statistical validation data. The model results illustrate a correlation coefficient (R²) of 0.999. In addition, the SVR results demonstrate the proposed model to be a fast tool and a robust approach to map input space to output features. The SVR model was compared to popular data-driven MMP estimation models as well. This comparison presents an acceptable accuracy relative to this estimation model. Finally, the presented model was evaluated against a comprehensive theoretical model of slim tube compositional simulation on a trusted literature dataset.