This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0.11 / km 2 , when considering military vehicles concealed in a forest.

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This is the published version of a paper published in IEEE Geoscience and Remote Sensing Letters. Citation for the original published paper (version of record):Renato, M. (2016) The stability of UWB low-frequency SAR images. Letters, 13(8) Access to the published version may require subscription. 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. Index Terms-Bivariate distribution, CARABAS-II, clutter, statistical modeling, very high frequency synthetic aperture radar (VHF SAR). IEEE Geoscience and Remote Sensing

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.Index Terms-Change detection, likelihood ratio test, synthetic aperture radar (SAR).

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This letter presents an incoherent change detection algorithm (CDA) for wavelength-resolution synthetic aperture radar (SAR) based on convolutional neural networks (CNNs). The proposed CDA includes a segmentation CNN, which localizes potential changes, and a classification CNN, which further analyzes these candidates to classify them as real changes or false alarms. Compared to state-of-the-art solutions on the CARABAS-II data set, the proposed CDA shows a significant improvement in performance, achieving, in a particular setting, a detection probability of 99% at a false alarm rate of 0.0833/km 2 .

This paper presents Bayes' theorem for wavelengthresolution SAR change detection method development. Different change detection methods can be derived using Bayes' theorem in combination with the target model, clutter-plus-noise model, iterative implementation, and non-iterative implementation. As an example of the Bayes' theorem use for wavelength-resolution SAR change detection method development, we propose a simple change detection method with a clutter-plus-noise model and non-iterative implementation. In spite of simplicity, the proposed method provides a very competitive performance in terms of probability of detection and false alarm rate. The best result was a probability of detection of 98.7% versus a false alarm rate of one per square kilometer.

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