The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Recently, compressive sensing theory has been successfully applied in inverse synthetic aperture radar (ISAR) imaging. However, the issue of maneuvering target imaging from compressive sampling data has not been sufficiently addressed because it is difficult to jointly deal with both sparse imaging and motion compensation under compressive sampling. In this paper, we develop a novel algorithm of high-resolution...
Efficient super-resolution of hyperspectral images (HSI) relies on the representational model (RM) that is capable of capturing the spatial and spectral correlation in hyperspectral images. In this paper, the spectral information in hyperspectral images is explained by linear spectral mixture model (LSMM), which expressed the observed pixels as a linear combination of endmembers, and the spatial information...
A novel background dictionary learning and structured sparse representation based anomaly detection method is proposed for hyperspectral imagery. First, a robust PCA spectrum dictionary is learned from the plausible background area detected by the local RX detector. With the learned dictionary, the reweighted Laplace prior based structured sparse representation model is then employed to reconstruct...
Based on sparsity or compressibility of microwave radiation images, Compressive Sensing in this paper is adopted to achieve microwave radiation imaging in order to reduce the complexity and hardware cost of the imaging system and get the image of high spatial resolution. Compared with the common wavelet basis, differential matrix is proposed to sparsely represent microwave radiation images. OMP algorithm...
In this paper, a sub-pixel mapping (SPM) method based on super-resolution then spectral unmixing (SRTSUSPM) is proposed. In the proposed framework, firstly projection onto convex set (POCS) model with the endmembers of interest is applied to original imagery to obtain a high-resolution imagery; then the fraction images are derived from the high-resolution imagery by linear spectral mixture analysis...
A three-component decomposition algorithm is proposed for polarimetric SAR data. After extracting the volume scattering component, both the orientation angle compensation and a unitary transformation are applied to the remaining matrix to derive the second and third components which are exactly consistent with either the surface scattering model or the double-bounce scattering model, respectively...
Hyperspectral image super resolution (SR) reconstruction has been studied widely and many algorithms have been proposed. In this paper, a novel super resolution reconstruction method was designed by employing a joint spectral-spatial sub-pixel mapping model which aims to obtain the probabilities of sub-pixels to belong to different land cover classes by dividing mixed pixels into several sub-pixels...
Low spatial and spectral resolution hyperspectral image will always degrade the performance of the subsequent applications, such as classification and object detection. The desired hyperspectral image is assumed to be reconstructed based on both high spatial and spectral features, which are always represented using endmembers and their abundances. In this paper, we propose a hyperspectral spatial...
Hyperspectral images play an important role in real-world applications, such as recognition and remote sensing, etc. How to enhance the spatial resolution of hyperspectral image is still a challenging problem in this field. In this paper, we propose a novel hyperspectral image super-resolution approach by jointly incorporating the sparse, low-rank constraints and spectral mixture priori into a linear...
Dual-Frequency Polarized Scatterometer (DFPSCAT) is a new system utilizing Doppler beam sharpening (DBS) technology for azimuthal resolution enhancement. Considering the DBS technology is inapplicable for the middle areas of the swath, a theoretical framework of deconvolution signal processing is proposed to improve resolution. A deconvolution method of the nonlocally centralized sparse representation...
Global Navigation Satellite Systems - Reflectometry (GNSSR) is an innovative and promising tool for remote sensing purposes. Several applications have been developed to extract geophysical information of the reflected scene from the measured delay waveforms and Delay-Doppler Maps (DDMs). Recently, techniques have been presented to deconvolve the DDMs to reconstruct the Normalize Radar Cross Section...
Efficient denoising of hyperspectral imagery (HSI) relies on an representational model that is capable of capturing the spatial and spectral correlation in HSI. Recently, an intrinsic representation (IR) approach based on the linear spectral mixture model (LSMM) was proposed for unsupervised feature extraction. The IR model constitutes a sound representational model due to its ability to account for...
Along with increasingly intense desire to achieve super high-resolution images, synthetic aperture radar (SAR) is facing more severe technical challenges such as sampling, storage and transmission of massive data as well as high complexity of hardware. Compressive sensing (CS) theory, which utilizes the signal sparsity, can implement accurate image reconstruction from an extremely less amount of measurements...
To utilize the spatial information and manifold structure in hyperspectral image (HSI), we propose a spatial-spectral manifold reconstruction classifier (SSMRC) for HSI classification in this paper. The SSMRC method firstly uses a mean filter to combine the spatial neighborhood information. Then the manifold reconstruction error utilizes as a measurement of how well a data point resides on a manifold,...
In this paper, we propose a multi-way projections-based reconstruction method for noise reduction of hyperspectral image (HSI). Core ideas of the proposed method are twofold: 1) the original HSI is partitioned into many small three-dimensional (3D) patches. Each of the patch is taken as a third-order tensor, on which compressive multi-way measurements are performed; 2) denoised patches are produced...
Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain 3-D scene properties and has broad application prospects. However, the reconstruction of cross-track dimension usually suffers from incomplete observation, which is caused by the non-uniformly and sparsely distributed virtual antenna phase centers. By formulating the cross-track reconstruction...
Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationship of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral image (HSI) that possesses many homogenous areas. In this paper,...
Near-field cross section imaging is an important issue in the area of near filed radar imaging. A conventional algorithm for cross section imaging is FFT after interpolation. It is intuitionistic and relatively fast. However the interpolation accuracy significantly affects the image quality especially when the data is highly curving in the case of wideband millimeter wave. To solve this problem, we...
Hyperspectral data compression and dimensionality reduction has received considerable interest in recent years due to the high spectral resolution of these images. Contrarily to the conventional dimensionality reduction schemes, the spectral compressive acquisition method (SpeCA) performs dimensionality reduction based on random projections. The SpeCA methodology has applications in Hyperspectral...
InSAR has been widely used in monitoring land subsidence over large area. However, many factors in InSAR processing, such as decorrelation error, atmosphere error, height error and thermal noise limit the accuracy of InSAR measurements. The height error over urban area is particularly the most difficult issue in TerraSAR-X data processing for its shorter wavelength and higher spatial resolution. We...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.