This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman-Pearson criterion. The first proposed method uses the data from wavelength-resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considers a priori information about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods. CCBY

This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59 & x0025; of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.

In this letter, we propose a method to reduce the number of false alarms in a wavelength-resolution synthetic aperture radar (SAR) change detection scheme by using a convolutional neural network (CNN). The detection is performed in two steps: change analysis and object classification. A simple technique for wavelength-resolution SAR change detection is implemented to extract potential targets from the image of interest. A CNN is then used for classifying the change map detections as either a target or nontarget, further reducing the false alarm rate (FAR). The scheme is tested for the CARABAS-II data set, where only three false alarms over a testing area of 96 km² are reported while still sustaining a probability of detection above 96%. We also show that the network can still reduce the FAR even when the flight heading of the SAR system measurement campaign differs by up to 100° between the images used for training and test. CCBY

The aim of this letter is to compare two incoherent change-detection algorithms for target detection in low-frequency ultrawideband (UWB) synthetic aperture radar (SAR) images. The considered UWB SAR operates in the frequency range from 20 to 90 MHz. Both approaches employ a likelihood ratio test according to the Neyman–Pearson criterion. First, the bivariate Rayleigh probability distribution is used to implement the likelihood ratio test function. This distribution is well known and has been used for change-detection algorithms in low-frequency UWB SAR with good results. Aiming to minimize the false alarm rate and taking into consideration that low-frequency UWB SAR images have high resolution compared to the transmitted wavelength, the second approach implements the test by using a bivariate K-distribution. This distribution has scale and shape parameters that can be used to adjust it to the data. No filter is applied to the data set images, and the results show that with a good statistical model, it is not needed to rely on filtering the data to decrease the number of false alarms. Therefore, we can have a better tradeoff between resolution and detection performance.

In this letter, we propose a novel method for unsupervised change detection in multitemporal multispectral Landsat images using multiobjective evolutionary algorithm (MOEA). The proposed method minimizes two different objective functions using MOEA to provide tradeoff between each other. The objective functions are used for evaluating changed and unchanged regions of the difference image separately. The difference image is obtained by using the structural similarity index measure method, which provides combination of the comparisons of luminance, contrast, and structure between two images. By evolving a population of solutions in the MOEA, a set of Pareto optimal solution is estimated in a single run. To find the best solution, a Markov random field fusion approach is used. Experiments on semisynthetic and real-world data sets show the efficiency and effectiveness of the proposed method.

Wave optics propagators (WOPs) are commonlyused to describe the propagation of radio signals through earth’satmosphere. In radio occultation (RO) context, multiple phasescreen (MPS) method has been used to model the effects of theatmosphere in Global Navigation Satellite System (GNSS) signalsduring an occultation event. WOP implementation includes,in addition to MPS, a diffraction integral as the final step tocalculate the radio signal measured in the low-earth orbit (LEO)satellite. This approach considers vacuum as the propagationmedium at high altitudes, which is not always the case when theionosphere is taken into account in simulations. An alternativeapproach is using MPS all the way to LEO in order to samplethe GNSS signal in orbit. This approach, named MPS orbitsampling (MPS-OS), is evaluated in this letter. Different scenariosof setting occultation assuming a short segment of the LEO orbithave been simulated using MPS and MPS-OS. Results have beencompared to Abel transform references. Furthermore, a longsegment scenario has been evaluated as well. A comparison ofbending angle (BA) and residual ionospheric error (RIE) showsthe equivalence between MPS and MPS-OS results. The mainapplication of MPS-OS should be in occultation events with longsegments of orbit and including ionosphere, in which a standardWOP may not be appropriate.

This letter proposes a regression model for nonnegative signals. The proposed regression estimates the mean of Rayleigh distributed signals by a structure which includes a set of regressors and a link function. For the proposed model, we present: 1) parameter estimation; 2) large data record results; and 3) a detection technique. In this letter, we present closed-form expressions for the score vector and Fisher information matrix. The proposed model is submitted to extensive Monte Carlo simulations and to the measured data. The Monte Carlo simulations are used to evaluate the performance of maximum likelihood estimators. Also, an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in synthetic aperture radar (SAR) images.

Wavelength-resolution (WR) synthetic aperture radar (SAR) change detection (CD) has been used to detect concealed targets in forestry areas. However, most proposed methods are generally based on matrix or vector analyses and, therefore, do not exploit information embedded in multidimensional data. In this letter, a CD method for WR SAR image stacks based on tensor robust principal component analysis (TRPCA) is proposed. The proposed CD method used the new tensor nuclear norm induced by the definition of the tensor-tensor product to exploit temporal and spatial information contained in the image stack. To assess the performance of the proposed method, we considered SAR images obtained by the very high frequency (VHF) WR CARABAS-II SAR system. Experiments for three different stack sizes show that a significant performance gain can be achieved when large image stacks are considered. The proposed CD method performs better in terms of probability of detection (PD) and false alarm rate (FAR) than the other five CD methods in VHF WR SAR images, including one based on matrix robust principal component analysis (RPCA). In a particular setting, it achieves a PD of 99% and a FAR of 0.028 false alarms per km2. Authors

This letter presents an analysis of prefiltered clutter ultrawideband (UWB) very high frequency synthetic aperture radar (SAR) images. The image data are reorganized into subvectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal for low-frequency SAR images. In this letter, it is found that, for “good” image pairs, the subtracted image has Gaussian distributed clutter backscattering and that the noise mainly consists of the thermal noise and, therefore, speckle noise does not have to be considered. This is a consequence of the stable backscattering for a UWB low-frequency SAR system.

This letter presents a wavelength-resolution syn-thetic aperture radar incoherentchange detection method basedon image stack, i.e., there are more than one reference or/andsurveillance image. Considering image stack in statistical hypoth-esis test for change detection is expected to result into a simplemathematical expression for implementation and provide betterchange detection results. As presented in this letter, a statisticalhypothesis test is developed on bivariate Gaussian distributionfor an image stack of two reference images and one surveillanceimage. The requirement for the image stack is three imagesassociated with three measurements with no change between twoof them. A detection method with simple processing scheme isproposed. The method is experimented with 24 CARABAS datasets. The results indicate that high average detection probability,e.g., 96%, with very low false alarm rate, e.g., only 0.19/km2,is obtained with the proposal.

This letter presents an incoherent change detectionalgorithm (CDA) for wavelength-resolution synthetic apertureradar (SAR) based on convolutional neural networks (CNNs).The proposed CDA includes a segmentation CNN, whichlocalizes potential changes, and a classification CNN, whichfurther analyzes these candidates to classify them as real changesor false alarms. Compared to state-of-the-art solutions on theCARABAS-II data set, the proposed CDA shows a significantimprovement in performance, achieving, in a particular setting,a detection probability of 99% at a false alarm rate of0.0833/km2

A 2-D spectrum for bistatic synthetic aperture radar is derived in this letter. The derivation is based on the commonly used mathematic principles such as themethod of stationary phase and the Fourier transform and the Lagrange inversion theorem in order to find the point of stationary phase in the method of stationary phase. Using the Lagrange inversion theorem allows minimizing the initial assumptions or the initial approximations. The derived 2-D spectrum is compared with the commonly used 2-D spectrum to verify it and illustrate its accuracy.

This letter introduces ground-range and cross-range resolution equations for the side-looking bistatic synthetic aperture radar (SAR). The derivation is based on the backprojection integral and the method of stationary phase. The ground-range and cross-range resolution equations are provided in closed form, making them easy for calculation. They are, therefore, helpful for bistatic SAR system development. The derived ground-range and cross-range resolution equations are validated with the bistatic data simulated mainly using the parameters of the LORA system. IEEE

This letter presents coherent and incoherent nonlinear apodization methods for bistatic synthetic aperture radar (SAR) imaging. The methods are developed on the principle of nonlinear apodization for monostatic SAR imaging and a ω - k relationship representing the bistatic region of support. This relationship plays an important role in designing 2-D windows for the apodization methods. The simulation results show that the presented apodization methods work efficiently with bistatic SAR imaging. In the experiment with a coherent dual apodization (CDA) method presented in this letter, the sidelobe attenuates very fast, and the peak sidelobe is reduced about 16 dB, while the resolutions in azimuth and range are maintained. CCBY

The tilt phenomenon or skew in bistatic SAR images has been known. This can be observed from the SAR image of a point-like scatterer, even with a symmetrical bistatic aperture. This letter explains this phenomenon in bistatic SAR images and shows that the phenomenon depends on the initial and the last positions of the transmitter and receiver platforms. A formula to calculate the tilt angle based on a general bistatic geometry is provided. This formula supports bistatic SAR system design and bistatic image quality measurements. Author

A 2-D spectrum for bistatic synthetic aperture radar is derived in this letter. The derivation is based on the commonly used mathematic principles such as themethod of stationary phase and the Fourier transform and the Lagrange inversion theorem in order to find the point of stationary phase in the method of stationary phase. Using the Lagrange inversion theorem allows minimizing the initial assumptions or the initial approximations. The derived 2-D spectrum is compared with the commonly

A new version of chirp scaling (CS), the so-called ultrawideband (UWB) CS (UCS), is proposed in this letter. UCS aims at UWB synthetic aperture radar (SAR) systems utilizing large fractional bandwidth and wide antenna beamwidth associated with a wide integration angle. Furthermore, it is also valid for SAR systems with special characteristics such as ground moving target indication SAR systems with a very high pulse repetition frequency.

In this letter, we propose an approach to suppress radio-frequency interference (RFI) in ultrawideband (UWB) low-frequency synthetic aperture radar (SAR). According to the proposal, RFI is suppressed by using an adaptive line enhancer controlled by the normalized least mean square algorithm. The approach is tested successfully on real UWB low-frequency SAR data. In order to keep the computational burden down, possible ways to integrate the RFI suppression approach into SAR imaging algorithms are also suggested.