Change detection in repeat pass interferometric synthetic aperture radar

Abstract

In repeat pass Synthetic Aperture Radar Interferometry (MAR) Scene disturbances may be identified as areas coefficient. The backscatter coefficient is dependent on the while the coherence is sensitive to changes in the distribution An examination of Q shows that a number of parameters of scattering elements within resolution cells. The coherence and may be used to discriminate between possible backscatter coefficient thus provide complementary information Scene disturbances and the undisturbed The RCS regarding possible disturbances in the underlying scattering bances using changes in backscatter coefficient or Coherence respectively are determined by " w a l and dielectric however is limited bv hieh false alarm rates unless sienificant moverties of the scatterine medium. Thus anv man-made topography [I]. The magnitude of the complex correlation coefficient y. termed the coherence, models the sources of of low coherence or possibly as areas of changed backscatter temporal change in the Scene [21 as well as decorrelation Structure and dielectric of the ,,,dum arising from system noise and processing aberrations [31.mechanisms. The ability to detect subtle man-made distur-parameters and of the primary and repeat pass images . I I aseraging is carried out resulting in a degradation in the resolution of the change maps. In this paper, models describing the changed and unchanged regions of a scene are postulated and the detection problem is expressed in a hypothesis testing framework. Forming the log likelihood ratio gives a single statistic, encoding both coherence and RCS changes, for discrimating between the unchanged and changed scene models. Expressions for the probability of detection and false alarm are derived for the likelihood ratio and show a significant improvement over both the RCS ratio and sample coherence change statistics. Finally the impmved detection performance is demonstrated using data collected in a repeat pass interferometry experiment with the DSTO Ingara X-baud airborne SAR.

Physics-Based Predictions for Coherent Change Detection Using X-Band Synthetic Aperture Radar

“…Given that γ snr , γ geom , and γ reg may be maintained constant or close to unity, the temporal variation in image coherency given in (23) will track the temporal variation in scene coherency γ scene , albeit at a lower level. In practice, this lower level is modelled as an equivalent coherency noise, and observationally this noise level is equivalent to approximately 7.2 dB below the HH backscattering coefficient [1]. Such a level of effective noise leads to a reduced coherency of γ = γ scene γ snr eff = γ scene 1 1 + σ n /σ HH = 0.84 (24) for the case of perfect scene coherence.…”

“…ROCs calculated in this way are displayed in Figure 6. Figure 6 also shows theoretical ROC curves for a loglikelihood change statistic [1,24,25] that may be evaluated for the case when the full 2 by 2 sample covariance matrix describing the interferometric image pair is available for discriminating between the changed and unchanged scenarios. The log-likelihood change statistic is based on the assumption that the N-pixel region under test in the repeat-pass image pair is described by a complex, circular, jointly Gaussian random process.…”

“…Coherent change detection (CCD) using synthetic aperture radar (SAR) images from repeated apertures is an interferometric SAR technique and an increasingly important and rapidly developing area of research [1,2,3]. CCD techniques may be used to detect scene disturbances that occur between repeated SAR observations, which would otherwise remain undetected by incoherent change detection techniques.…”

Laboratory bistatic synthetic aperture radar coherent change detection investigation

“…The length of $${\bf{\vec{K}}}$$ is proportional to frequency, and to the cosine of the bistatic angle [1]. In the context of coherence between SAR images of horizontal terrain the proportion of the overlapping vertically projected image support area to total projected areas ( K ‐space overlap) [4] can be used to predict coherence. In the SAR far‐field it is straightforward to calculate K ‐space overlap.…”

A Coherence Improvement Method Based on Sub-Aperture InSAR for Human Activity Detection

“…SAR system is distinct from other sensors in that it can obtain not only the amplitude information of the target, but also the phase information. There are normally two detection methods [ 14 ]: one is the incoherent change detection method based on amplitude information—this method can only detect large-scale activities, such as parking cars, truck track and so on, but it cannot detect small-scale activities due to the limitation of resolution; the other is the coherent change detection (CCD) method based on phase information, which can detect small-scale activities [ 15 , 16 , 17 ] such as human footprints, car track, grazing and other very subtle human activities. This is explained by the fact that the phase information acquired by the SAR system is very sensitive to subtle changes.…”