EO·Atlas
SAR coherent change detection (CCD)
Detects fine-scale surface disturbance by measuring the loss of interferometric phase coherence between two SAR acquisitions, sensitive to changes too subtle to alter backscatter amplitude. Used for damage assessment and disturbed-ground mapping.
SAR coherent change detection (CCD) answers the question: has fine-scale surface structure been disturbed between two radar acquisitions? Unlike amplitude-based change detection, which measures differences in backscatter intensity, CCD measures the loss of interferometric phase coherence between two single-look complex (SLC) SAR images acquired from the same repeat-pass geometry. Small changes that do not alter the gross backscatter amplitude, such as disturbed soil, moved objects, or structural damage, are detectable because they randomise the phase relationship between the two acquisitions.[1]
The operational form of the algorithm computes the coherence magnitude between a pre-event SLC pair and a co-event SLC pair. A stack of pre-event image pairs builds a per-pixel baseline distribution of expected coherence as a function of temporal baseline (mean and variance). The co-event coherence is then compared against this baseline to estimate the probability of change.[2] The method requires precise repeat-pass geometry (same or near-identical look angle and incidence angle) and a temporal baseline short enough to limit natural decorrelation. The ICEYE Daily Ground Track Repeat (GTR) constellation is designed specifically to acquire same-geometry SAR images at the cadence required for coherent change detection at scale.[3]
CCD is applied to damage assessment, disturbed-ground mapping, and humanitarian monitoring. The method fails on vegetated surfaces (temporal decorrelation from wind and growth), open water (low coherence baseline regardless of change), and urban surfaces during heavy rainfall.
- MetaSAR-L
MetaSAR-L bistatic configuration enables coherent change detection with stable phase
Detects land-cover or structural change by comparing backscatter intensity across dates; good for flood mapping, deforestation alerts, and disaster damage assessment.
Flags surface change by testing each new optical observation against a per-pixel statistical baseline built from the historical time-series, accumulating evidence across dates before confirming a change. Underlies near-real-time deforestation and disturbance alert systems.
Maps the extent and timing of fire-affected area by tracking persistent post-fire changes in surface reflectance across a time-series, often anchored by coincident active-fire detections. Produces systematic burned-area records.
- [1]Coherent Change Detection: Theoretical Description and Experimental Resultsagency doc2026-06-04(2006) DSTO-TR-1851; foundational statistical description of CCD; establishes coherence magnitude as change statistic; two-pass repeat geometry X-band experiment
- [2]Damage Proxy Mapping with SAR interferometric coherence changepeer reviewed2026-06-04(2024) Describes coherence-loss DPM: pre-event pair coherence vs co-event pair coherence; statistical comparison; pre-event stack as baseline for per-pixel statistics
- [3]Beyond Change Detection: Measuring the Changes that Matteroperator datasheet2026-06-04(2023) ICEYE GTR constellation design for coherent change detection; Daily GTR enables deep pre-event coherence stacks. Tier 6 framing but confirms operational product exists.
Edited from public sources. Last reviewed date pending by SpectraWorks editorial. See the data dictionary for field definitions.