Abstract
The log-ratio (LR) operator is well suited for change detection in synthetic aperture radar (SAR) amplitude or intensity images. In applying the LR operator to change detection in multi-temporal SAR images, a crucial problem is how to develop precise models for the LR statistics. In this study, we first derive analytically the probability density function (PDF) of the LR operator. Subsequently, the PDF of the LR statistics is parameterized by three parameters, i.e., the number of looks, the coherence magnitude, and the true intensity ratio. Then, the maximum-likelihood (ML) estimates of parameters in the LR PDF are also derived. As an example, the proposed statistical model and corresponding ML estimation are used in an operational application, i.e., determining the constant false alarm rate (CFAR) detection thresholds for small target detection between SAR images. The effectiveness of the proposed model and corresponding ML estimation are verified by applying them to measured multi-temporal SAR images, and comparing the results to the well-known generalized Gaussian (GG) distribution; the usefulness of the proposed LR PDF for small target detection is also shown.
Highlights
During the last two decades, the problem of change detection [1] from at least two geo-registered synthetic aperture radar (SAR) images, collected at different times, has been recognized to be important for practical applications related to environmental monitoring [2,3,4], damage assessment [5,6], urban studies [7,8], and forest monitoring [9,10,11]
The objective of this paper is to address the aforementioned issues with respect to the characterization of LR statistics and the corresponding parameter estimates
This table clearly confirms the conclusion that were drawn by visual inspection, i.e., the proposed model is in better agreement with the histograms of the LR statistics for different
Summary
During the last two decades, the problem of change detection [1] from at least two geo-registered synthetic aperture radar (SAR) images, collected at different times, has been recognized to be important for practical applications related to environmental monitoring [2,3,4], damage assessment [5,6], urban studies [7,8], and forest monitoring [9,10,11]. To obtain the changed and unchanged pixels between two SAR images, the most popular way to use the LR operator is to employ statistical approaches by linking the distribution model of the LR statistics and a specific threshold decision technique, e.g., the expectation maximization (EM) [26] and Kittler–Illingworth (KI) threshold methods [27,28,29,30]. The change detection of vehicles based on the measured data is assessed by combining the proposed parameter estimation and the CFAR technique. The usefulness of the proposed distribution model and the effectiveness of the corresponding parameter estimation are verified for small target detection.
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