Hyperspectral image classification has attracted considerable interest in recent years. The previous classification methods are usually based on single-kernel or composite-kernel machines. In this paper, a novel regularization framework referred to least-squares support vector machine in sum space (LS-SVM-SS) of reproducing kernel Hilbert space (RKHS) is proposed for the classification of hyperspectral images using spectral signatures or local binary pattern features. The method is designed to simultaneously approximate the low- and high-frequency components of the target classification function with multiscale kernels. In the newly proposed scheme, LS-SVM-SS carries out the supervised learning and train a closed-form discriminant function to directly implement the multiclass classification. In contrast to multiple-kernel learning (MKL) by one-step method or two-step method, we can obtain a noniterative optimization procedure. Experiments are conducted on three real hyperspectral datasets. The corresponding experimental results demonstrate that the LS-SVM-SS method achieves good generalization performance in most cases compared with several state-of-the-art methods, which is better than that in any single-kernel RKHS. In addition, the proposed framework of multiscale kernels classifier opens a wide field for future developments in hyperspectral image classification.

With a view to detecting foliage-obscured/ground-obscured targets on a global scale, low frequency (i.e., very high frequency (VHF)/ultrahigh frequency (UHF) band) and wide bandwidth is a trend in future spaceborne synthetic aperture radar (SAR) system design. However, due to the dispersion of ionosphere, VHF/UHF wide bandwidth SAR signals inevitably experience adverse effects. In contrast to narrow bandwidth SAR at VHF/UHF, quadratic and cubic ionospheric phase errors will introduce noticeable effects on future wide-bandwidth SAR systems. Traditional evaluation models based on Taylor series expansion may become inaccurate when an extremely wide bandwidth is considered. With a focus on this this issue, first, the shortcoming of Taylor series expansion of ionospheric phase errors is briefly discussed in this paper. Then, a new analytical model based on Legendre orthogonal polynomials is developed, which is expected to be widely applicable, especially for low-frequency and wide-bandwidth SAR systems. Finally, compared with previous models based on Taylor series expansion, numerical simulations and evaluations show the superiority of the new model.

This paper discusses the degradation of space-borne synthetic aperture radar (SAR) imaging due to anisotropic ionospheric irregularities. In the case of the oblique incidence and anisotropy of ionospheric irregularities, two types of potential ionospheric effects on SAR imaging are derived from an improved transverse correlation function. First, in the range direction, the image shift due to both multiple scattering and dispersion of irregularities is considered. In addition, pulse broadening due to multiple scattering of the irregularities is also studied, which leads to degradation of the range resolution. Second, in the azimuthal direction, the decorrelation distance is obtained by the second moment of the generalized ambiguity function. Using the range Doppler algorithm (RDA), several degraded point target responses due to the ionospheric irregularities are evaluated. The simulation results show that the adverse effects of ionospheric irregularities on SAR imaging are worsened at lower frequencies and stronger ionospheric fluctuations.

The ionosphere is the Earth’s upper atmosphere with sufficiently charged particles that influence the electromagnetic wave propagation, thereby impeding spaceborne synthetic aperture radar (SAR) observations. High precision imaging and the measurements of geographic and geophysical processes using interferometric SAR (InSAR) and polarimetric SAR (Pol-SAR) are hampered. This study presents a detailed and comprehensive review of the research on ionospheric effects on SAR, InSAR, and Pol-SAR, their correction approaches, and reverse guidance for ionospheric sounding. The prospect is raised at the end of the paper, and the ionosphere will continue to be a research hotspot in the remote sensing community due to the development of low-frequency SAR satellites. In addition, applying spaceborne SAR to ionospheric sounding is a promising issue for space weather and geospace physics due to high spatiotemporal resolution and abundant parameter information.

The interest in the use of spaceborne synthetic aperture radar (SAR) for collecting earth bio/geophysical information and detecting foliage-obscured targets has been increased. However, the signals are inevitably affected by the ionosphere, particularly at very high frequency and ultrahigh frequency. Thus, it is crucial to understand the potential effects of the ionosphere on SAR systems. In this paper, three possible contributions are made to analyze these effects. First, for analyzing range resolution, in addition to linear and quadratic phase errors due to the background ionosphere, the cubic phase error is considered. The expected mean electron density inferred from the International Reference Ionosphere is used. Second, for analyzing azimuthal resolution, the effects of ionospheric irregularities are evaluated under the conditions of oblique incidence and anisotropic irregularities. The model is presented on the basis of the multiple phase screen (MPS) method. Compared with the previous model, the MPS method can give results in good agreement with both weak and strong scattering theories. Finally, based on the theory of moment equation, range resolution degradation caused by the multiple scattering is also studied in the case of anisotropic irregularities. By using the range Doppler algorithm, a number of degraded point responses due to these ionospheric effects are shown, and then, some evaluation results are listed.