This paper introduces a method to reduce false alarms in wavelength-resolution synthetic aperture radar (SAR) change detection and aims at very high frequency-band systems like the Coherent All Radio Band System (CARABAS). The false alarms are usually caused by the elongated structures, such as power lines and fences, which stand out from the background. The responses of elongated structures are sensitive to flight path. The introduced method aims at minimizing the false alarms caused by the elongated structures and is based on the well-known adaptive processing mechanism, i.e., the so-called adaptive noise canceler (ANC) where a separate reference signal is required. The changes between measurements are considered by the input signal of ANC while the separate reference signal comes from the measurements without change. Hence, the method requires three SAR images associated with three measurements, with no changes between two of them. The reference data for the study are provided by CARABAS. The experimental results indicate that the method can reduce false alarms significantly and provide high probability of detection (≥98%). The experimental results also show that the method still works well even in the case where the flight tracks of the SAR system in the change detection measurements are slightly different.

A gamma probability density function (pdf) is shown to be an alternative to model the distribution of the magnitudes of high-resolution, i.e., wavelength-resolution, synthetic aperture radar (SAR) images. As investigated in this paper, it is more appropriate and more realistic statistical in comparison with, e.g., Rayleigh. A bivariate gamma pdf is considered for developing a statistical hypothesis test for wavelength-resolution incoherent SAR change detection. The practical issues in implementation of statistical hypothesis test, such as assumptions on target magnitudes, estimations for scale and shape parameters, and implementation of modified Bessel function, are addressed. This paper also proposes a simple processing scheme for incoherent change detection to validate the proposed statistical hypothesis test. The proposal was experimented with 24 CARABAS data sets. With an average detection probability of 96%, the false alarm rate is only 0.47 per square kilometer.

This letter presents a wavelength-resolution synthetic aperture radar incoherent change detection method based on image stack, i.e., there are more than one reference or/and surveillance image. Considering image stack in statistical hypothesis test for change detection is expected to result into a simple mathematical expression for implementation and provide better change detection results. As presented in this letter, a statistical hypothesis test is developed on bivariate Gaussian distribution for an image stack of two reference images and one surveillance image. The requirement for the image stack is three images associated with three measurements with no change between two of them. A detection method with simple processing scheme is proposed. The method is experimented with 24 CARABAS data sets. 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 paper addresses the use of a data analysis tool, known as robust principal component analysis (RPCA), in the context of change detection (CD) in ultrawideband (UWB) very high-frequency (VHF) synthetic aperture radar (SAR) images. The method considers image pairs of the same scene acquired at different time instants. The CD method aims to maximize the probability of detection (PD) and minimize the false alarm rate (FAR). Such aim fits into a multiobjective optimization problem, since maximizing the probability of detection generally implies an increase in the number of false alarms. In that sense, varying the RPCA regularization parameter leads to PD variation with respect to FAR, which is known as receiver operating characteristic (ROC) curve. To evaluate the proposed method, the CARABAS-II data set was considered. The experimental results show that RPCA via principal component pursuit (PCP) can provide a good trade-off between PD and FAR. A comparison between the results obtained with the proposed method and a classical CD algorithm based on the likelihood ratio test provides the pros and cons of the proposed method.

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% 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.