Isin Erer

Bilateral Filtering-Based Enhanced Pansharpening of Multispectral Satellite Images

An efficient pansharpening method should inject the missing geometric information to the multispectral (MS) image while preserving its radiometric information. Widely used additive wavelet transform-based pansharpening methods extract the missing high-frequency information by decomposing the panchromatic (PAN) image and adding the detail layers to the low-resolution MS (LRM) image. However, this approach causes a redundant detail injection, leading to artifacts in the fusion result. In this letter, we propose to decompose the high-resolution-PAN image using an edge-preserving decomposition which will decrease the amount of redundant high-frequency injection. The missing high-frequency information of the LRM image is obtained by the decomposition of the PAN image using a multiscale bilateral filter. The spatial and range parameters of the bilateral filter are optimized so as to enhance spatial and spectral metrics. The fusion results are compared with the widely used additive wavelet luminance proportional (AWLP) and recently proposed improved AWLP fusion methods. The resulting images as well as evaluation metrics demonstrate that the proposed injection approach has better performance.

Optimum asymmetric half-plane autoregressive lattice parameter modeling of 2-D fields

In this paper, we present a new optimum asymmetric half-plane (ASHP) autoregressive lattice parameter modeling of two-dimensional (2-D) random fields. This structure introduces 4N points into the prediction support region when the order of the model increases from (N-1) to N. Starting with a given data field, a set of four auxiliary prediction errors are generated in order to obtain the growing number of 2-D ASHP reflection coefficients at successive stages. The theory has been applied to the high-resolution radar imaging problem and has also been proven using the concepts of vector space, orthogonal projection, and subspace decomposition. It is shown that the proposed ASHP structure generates the orthogonal realization subspaces for different recurse directions. In addition to developing the basic theory, the presentation includes a comparison between the proposed theory and other alternative structures, both in terms of conceptual background and complexity. While the recently developed reduced-complexity ASHP lattice modeling structure requires O(4N/sup 3/) lattice sections with N equal to the order of the error filter, the proposed configuration requires only O(2N/sup 2/) lattice sections.

Clutter Removal in Ground-Penetrating Radar Images Using Morphological Component Analysis

Ground-penetrating radar (GPR) is one of the most popular subsurface sensing devices and has a wide range of applications, e.g., target detection. It is well known that the target detection process in the GPR is highly affected by clutter. Especially, in the case of landmine detection, since targets are located near the surface, a target signal may be completely covered by the clutter. Thus, clutter reduction must be performed prior to any target detection scheme in the GPR. Singular value decomposition, principal component analysis, and independent component analysis are commonly used for clutter removal. They all aim to decompose the GPR images into subcomponents that represent the clutter and the target separately. In this letter, we propose a sparse model for differentiating the target and the clutter using appropriate dictionaries based on morphological component analysis (MCA). Calculated sparse coefficients and corresponding dictionaries are used to reconstruct the clutter and the target components. Visual and quantitative results validate that the proposed MCA-based method has higher performance than the state-of-the-art clutter reduction methods.

A novel DFT/RDFT based subband representation for the fusion of remote sensing images

We present a new fusion algorithm using a novel multidecomposition approach based on a DFT/RDFT-based symmetric, zero-phase, nonoverlapping digital filter bank representation. For the panchromatic image and each band of the multispectral image, the lowpass and highpass subband components are obtained in a tree structure using zero-phase filters implemented by the RDFT. The approximation subband of the multispectral image is merged with the detail subbands of the panchromatic image. The fused data is recovered from the subband signals by a new perfect reconstruction algorithm. This process is performed on each band of the multispectral image to obtain the final fused high resolution multispectral image.

Speckle noise reduction in SAR imaging using lattice filters based subband decomposition

A new speckle reduction algorithm based on lattice filters for SAR imaging is presented. In the new method, the subband decomposition of the speckled image is performed using lattice filters. The noisy image is decomposed into subband images using high-pass and low-pass filters having lattice structure, then a threshold value is estimated according to noise variance in each subband and soft-thresholding is applied on the subband images. The despeckled image is obtained from the thresholded subband images using the inverse lattice filter. The proposed speckle reduction method is applied to RADARSAT/SAR images. The performance of the proposed method has also been compared with median filtering, and discrete and stationary wavelet transform based speckle reduction methods. Results show that the proposed method may be used efficiently for speckle noise reduction in SAR images.

A comparative study on clutter reduction techniques in GPR images

Singular value decomposition (SVD), principal component analysis (PCA), and independent component analysis (ICA) are used as subspace based methods and curvelet transform (CT), nonsubsampled contourlet transform (NSCT) are used as multi-resolution based methods for clutter reduction algorithms in Ground-Penetrating Radar (GPR) images have been proposed in recent years with demonstrated success. However, the datasets for evaluated clutter reduction algorithms are not the same and using different setups. Thus, the performance result of algorithms in the literature are incomparable and sometimes contradictory. To address these problems, we design an extensive evaluation for the well-known clutter reduction algorithms with various scenarios to understand how these methods perform within the same framework. The methods are evaluated on the simulated data generated by the GprMax program. A large library of simulated data is constructed by changing the three crucial parameters such as soil types, burial depths and material types in order to analyze the methods in depth. The performance of both groups of methods are evaluated and results are reported in the sense of peak signal-to-noise (PSNR) for various scenarios.

DFT/RDFT bank approach for speckle reduction in SAR images

A new speckle reduction algorithm based on subband decomposition is presented. The subband decomposition is performed using a DFT/RDFT-based symmetric, zero-phase, nonoverlapping digital filter bank. The SAR image is decomposed using the proposed filter bank structure and a threshold value is estimated according to noise variance in each subband, and used for soft thresholding. The despeckled image is obtained from the thresholded subband coefficients by a new perfect reconstruction algorithm. Experimental results show that the proposed method has competitive noise removal and detail preservation properties as compared to conventional approaches such as median filtering and wavelet transform based methods.

High resolution radar imaging using GPOF based data extrapolation

A new data extrapolation technique which utilizes pole extraction based GPOF method is proposed to fit the scattering characteristics of an object. The proposed data extrapolation method is very efficient for data extrapolation when applied to the ISAR data. Modeling the data as the superposition of complex exponential signals, GPOF and Prony methods do not guarantee a stable prediction filter like the MCM method, while Burg ensures. Meanwhile, the performance of GPOF decreases with the increasing number of scattering centers but the radar images obtained using this extrapolated data is still more accurate than those obtained using the extrapolated data generated by other methods

A comparison of non-parametric spectral estimators for SAR imaging

Nonparametric spectral estimators have many applications including target range signature estimation and synthetic aperture radar (SAR) imaging. In this paper we explain how to apply nonparametric approaches to SAR imaging. We present an important nonparametric spectral estimation method which is the Capon algorithm. We compare the Capon method with other nonparametric methods such as APES, Periodogram, and FFT for their resolution, sidelobe levels, and spectral peaks. And we also show by means of experimental examples that the forward-backward Capon gives much better estimation than the forward only Capon.

Sparse representations based clutter removal in GPR images

In GPR system, the reflected signal is composed of three components; clutter, target signal and system noise. As system noise has less importance compared to the other components, clutter reduction methods aim to decompose the reflected signal as target signal and clutter. In this paper, target signal and clutter are modeled sparsely with appropriate dictionaries via morphological component analysis. Resulting sparse coefficients and corresponding dictionaries are used to reconstruct clutter and target components. The proposed method is applied to experimental B-scan data and it is shown that the results have higher performance compared to the widely used Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) based clutter reduction methods.