Analysis of Hypersonic Platform-Borne SAR Imaging: A Physical Perspective

The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization of air. The ionized gas forms the plasma sheath wrapped around the hypersonic platform. The plasma sheath will severely affect the propagation of SAR signal and further affect the SAR imaging. Therefore, hypersonic platform-borne SAR imaging should be studied from a physical perspective. In this paper, hypersonic platform-borne SAR imaging under plasma sheath is analyzed. The SAR signal propagation in plasma sheath is computed using scatter matrix method. The proposed SAR signal model is verified by using a ground experiment system. Moreover, the effect of attenuation caused by plasma sheath on SAR imaging is studied under different SAR parameters and plasma sheath. The result shows that attenuation caused by plasma sheath will degrade the SAR imaging quality and even cause the point and area targets to be submerged into the noise. The real SAR images under plasma sheath also illustrate this phenomenon. Furthermore, by studying imaging results under different SAR and plasma parameters, it can be concluded that the severe degradation of SAR imaging quality appears at condition of high plasma sheath electron density and low SAR carrier frequency. The work in this paper will be beneficial for the study of hypersonic platform-borne SAR imaging and design of hypersonic SAR imaging systems in the future.

Correction to: Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the Student t distribution

A Correction to this paper has been published: 10.1007/s11075-021-01156-z

Analytical versus numerical solutions of the nonlinear fractional time–space telegraph equation

In this paper, the stable analytical solutions’ accuracy of the nonlinear fractional nonlinear time–space telegraph (FNLTST) equation is investigated along with applying the trigonometric-quantic-B-spline (TQBS) method. This investigation depends on using the obtained analytical solutions to get the initial and boundary conditions that allow applying the numerical scheme in an easy and smooth way. Additionally, this paper aims to investigate the accuracy of the obtained analytical solutions after checking their stable property through using the properties of the Hamiltonian system. The considered model for this study is formulated by Oliver Heaviside in 1880 to define the advanced or voltage spectrum of electrified transmission, with day-to-day distances from the electrified communication or the application of electromagnetic waves. The matching between the analytical and numerical solutions is explained by some distinct sketches such as two-dimensional, scatter matrix, distribution, spline connected, bar normal, filling with two colors plots.

Clonality in Invasive Alien Macrophytes in Kashmir Himalaya: A Stage Based Approach

BackgroundThe invasion of non-native species is a major cause of the global biodiversity loss and creates enormous economic costs. What determines alien invasive species dominance over native plant species is still little known, but there is an emerging pattern that many of the world’s worst invasive plants are successful invaders due to the significant connection between their clonal traits and invasiveness. Freshwater ecosystems are relatively more prone to decline and extinction of species caused by biological invasion than terrestrial and marine ecosystems. In view of the lack of information about whether or not aquatic alien species at different stages of invasion exhibit any significant relation with clonality, the specific question addressed in this study was whether there is any relationship between clonality and invasiveness in aquatic macrophytes and how does it vary along different stages of invasion? ResultsWhile the link between clonality and species invasiveness has recently been recognized, whether and how clonality varies with different invasion-stages remains open questions. Hence, we tested the relationship between clonality and species invasiveness of Kashmir Himalayan aquatic macrophytes vis-à-vis its variability along different stages of invasion. The data on clonality, stage of invasion, and growth form was obtained through an extensive survey of literature and database like CLO-PLA (CLOnal PLAnts, version 3) and PLADIAS (Plant Diversity Analysis and Synthesis, 2014–2018) followed by evaluation of the clonal organs thorough intensive field surveys undertaken over a period of 3 years (2014-2017) in different aquatic habitats of the Kashmir valley. Our results showed that 84% of the studied species and almost 90% of the most invasive species (stage V sensu Colautti and MacIsaac 2004) are clonal. A strong positive correlation (r=0.94; p<0.05) between clonality and invasiveness was observed, which further substantiates this association at a broad geographical scale representing the whole region. From the scatter matrix and Pearson’s correlation matrix, clonality seems to have a strong positive correlation with fragments, rhizomes, runners, turions, tubers, and buds, thereby further affirming the strong nexus of clonality with species invasions.ConclusionsOur results showed strong association of clonal architecture with not only the stages of invasion but also the distribution pattern of alien species in aquatic habitats, thereby indicating the pivotal contribution of clonality to invasiveness. Future studies directed at unraveling the reasons behind clonality need to be undertaken from the genomic perspective, in order to evolve better models for proper management of alien aquatic invasive species.

Feature Line Embedding Based on Support Vector Machine for Hyperspectral Image Classification

In this paper, a novel feature line embedding (FLE) algorithm based on support vector machine (SVM), referred to as SVMFLE, is proposed for dimension reduction (DR) and for improving the performance of the generative adversarial network (GAN) in hyperspectral image (HSI) classification. The GAN has successfully shown high discriminative capability in many applications. However, owing to the traditional linear-based principal component analysis (PCA) the pre-processing step in the GAN cannot effectively obtain nonlinear information; to overcome this problem, feature line embedding based on support vector machine (SVMFLE) was proposed. The proposed SVMFLE DR scheme is implemented through two stages. In the first scatter matrix calculation stage, FLE within-class scatter matrix, FLE between-scatter matrix, and support vector-based FLE between-class scatter matrix are obtained. Then in the second weight determination stage, the training sample dispersion indices versus the weight of SVM-based FLE between-class matrix are calculated to determine the best weight between-scatter matrices and obtain the final transformation matrix. Since the reduced feature space obtained by the SVMFLE scheme is much more representative and discriminative than that obtained using conventional schemes, the performance of the GAN in HSI classification is higher. The effectiveness of the proposed SVMFLE scheme with GAN or nearest neighbor (NN) classifiers was evaluated by comparing them with state-of-the-art methods and using three benchmark datasets. According to the experimental results, the performance of the proposed SVMFLE scheme with GAN or NN classifiers was higher than that of the state-of-the-art schemes in three performance indices. Accuracies of 96.3%, 89.2%, and 87.0% were obtained for the Salinas, Pavia University, and Indian Pines Site datasets, respectively. Similarly, this scheme with the NN classifier also achieves 89.8%, 86.0%, and 76.2% accuracy rates for these three datasets.