Proc. SPIE. 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
KEYWORDS: 3D acquisition, 3D image reconstruction, Detection and tracking algorithms, Data modeling, Stereoscopy, Synthetic aperture radar, Image restoration, 3D modeling, Reconstruction algorithms, 3D image processing
There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l0 norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l0 norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.
Owing to the high spectral sampling, the spectral information in hyperspectral imagery (HSI) is often highly correlated and contains redundancy. Motivated by the recent success of sparsity preserving based dimensionality reduction (DR) techniques in both computer vision and remote sensing image analysis community, a novel supervised nonparametric sparse discriminant analysis (NSDA) algorithm is presented for HSI classification. The objective function of NSDA aims at preserving the within-class sparse reconstructive relationship for within-class compactness characterization and maximizing the nonparametric between-class scatter simultaneously to enhance discriminative ability of the features in the projected space. Essentially, it seeks for the optimal projection matrix to identify the underlying discriminative manifold structure of a multiclass dataset. Experimental results on one visualization dataset and three recorded HSI dataset demonstrate NSDA outperforms several state-of-the-art feature extraction methods for HSI classification.
Image matching has always been a very important research areas in computer vision. The performance will directly affect the matching results. Among local descriptors, the Scale Invariant Feature Transform(SIFT) is a milestone in image matching, while HOG as an excellent descriptor is widely used in 2D object detection, but it seldom used as a descriptor for matching. In this article, we suppose to pool these algorithms and we use a simple modification of the Rotation- Invariant HOG(RI-HOG) to describe the feature domain detected by SIFT. The RI-HOG is Fourier analyzed in the polar/spherical coordinates. Later in our experiment, we test the performance of our method on a datasets. We are surprised to find that the method outperforms other descriptors in image matching in accuracy.
This paper proposes an algorithm of simulating spatially correlated polarimetric synthetic aperture radar (PolSAR) images based on the inverse transform method (ITM). Three flexible non-Gaussian models are employed as the underlying distributions of PolSAR images, including the KummerU, W and M models. Additionally, the spatial correlation of the texture component is considered, which is described by a parametric model called the anisotropic Gaussian function. In the algorithm, PolSAR images are simulated by multiplying two independent components, the speckle and texture, that are generated separately. There are two main contributions referring to two important aspects of the ITM. First, the inverse cumulative distribution functions of all the considered texture distributions are mathematically derived, including the Fisher, Beta, and inverse Beta models. Second, considering the high computational complexities the implicitly expressed correlation transfer functions of these texture distributions have, we develop an alternative fast scheme for their computation by using piecewise linear functions. The effectiveness of the proposed simulation algorithm is demonstrated with respect to both the probability density function and spatial correlation.
Point set registration is a key component in many computer vision tasks. This paper proposes a point set registration algorithm based on information geometry. Point sets to be registration are converting to the statistical manifolds by Gaussian mixture model. The component of mixture model represents the dimension of statistical manifold and point set is a point on manifold. Through conversion, point set registration is reformulated as searching the shortest path between two manifold and we can use the em algorithm which defined by information geometry to get the optimization solution. Experimental results show that the proposed algorithm is robust to noise and outliers, and achieved very good accuracy.
Precise segmentation is crucial for the feature extraction and classification of ships in SAR imagery. To alleviate the Doppler shift and the cross ambiguity, this paper propose to segment the ship area from its background based on the radon transform. Assuming that the region of interest (ROI) of ship in SAR imagery has been extracted, the detail procedures of the proposed refined segmentation can be summarized as follows. First, the ship’s ROI image is transformed to radon domain, in which pixel intensities are cumulated along different directions. Then, the peak areas are separated to extract the ship’s orientation and the main image area of the ship that orthogonal to the principal axis. Finally, the refined segmentation is achieved in the main image area. Experiments, accomplished over measured medium and high resolution SAR ship images, show the effectiveness of the proposed approach.
As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation has attracted much attention in target classification recently. In this paper, we develop a new SAR vehicle classification method based on sparse representation, in which the correlation between the vehicle’s aspect angle and the sparse representation coefficients is exploited. The detail procedure presented in this paper can be summarized as follows. Initially, the sparse coefficient vector of a test sample is solved by sparse representation algorithm with a pixel based dictionary. Then the coefficient vector is projected onto a sparser one with the constraint of vehicle’s aspect angle. Finally, the vehicle is classified to a certain category that minimizes the reconstruct error with the sparse coefficient vector. We present promising results of applying the proposed method to the MSTAR dataset.
Ship detection is significant especially with the increasing worldwide cooperation in commerce and military affairs.
Space-borne Synthetic Aperture Radar (SAR) is optimal for ship detection due to its high resolution over wide swaths
and all-weather working capability. Constant False Alarm Rate (CFAR) detection of ships in SAR imagery is a robust
and popular choice. K distribution has been widely accepted for homogeneous sea clutter modeling. Although localized
K-distribution based CFAR detection has been developed to solve the non-homogeneous problem, it is not satisfied
under adverse conditions, for example, interference target appears in the background window. In order to overcome its
shortcomings, this paper presents an adaptive algorithm to improve the performance. It mainly includes the homogeneity
assessment of the local background area and the automatic selection between the localized K-distribution-based CFAR
detector and the OS-CFAR detector, which has better detecting performance in non-homogeneous situation. The theory
is investigated in detail firstly, and then experiments are carried out and the results illustrate that the novel algorithm
outperforms the state-of-art methods especially under complex sea background condition.
SAR images simulation is very important for SAR image interpretation and target recognition, and the method of SAR
images simulation of ground vehicles is thoroughly studied in this paper. Firstly, the dominant scattering mechanisms of
ground vehicles are analyzed. Secondly the RCS and complex scattering field of targets is computed based on
high-frequency electromagnetic scattering prediction methods of PO and GO etc. And finally, through SAR imaging
processing of the complex scattering field datum, the simulated SAR images are obtained. The performance of the
simulation is evaluated qualitatively and quantitatively by comparing the simulated SAR images with MSTAR measured
ones. And they all show good agreement.
Information contained in fully polarimetric SAR data is plentiful. How to exploit the information to improve accuracy is
important in segmentation of fully polarimetric SAR images. Several frequently used feature vectors and methods are
investigated, and a novel method is proposed for segmenting multi-look fully polarimetric SAR images in this paper,
starting from the statistical characteristic and the interaction between adjacent pixels. In order to use fully the statistical a
priori knowledge of the data and the spatial relation of neighboring pixels, the Wishart distribution of the covariance
matrix is integrated with the Markov random field, then the iterated conditional modes (ICM) is taken to implement the
maximum a posteriori estimation of pixel labels. Although the ICM has good robustness and fast convergence, it is
affected easily by initial conditions, so the Wishart-based ML is used to obtain the initial segmentation map, in order to
exploit completely the statistical a priori knowledge in the initial segmentation step. Using multi-look fully polarimetric
SAR images, acquired by the NASA/JPL AIRSAR sensor, the new approach is compared with several other commonly
used ones, better segmentation performance and higher accuracy are observed.