Vehicle Trace Detection in Two-Pass SAR Coherent Change Detection Images With Spatial Feature Enhanced Unet and Adaptive Augmentation

As a typical application of remote sensing technology, change detection can find the ground information changes by acquiring the images of the same region at different times. The change detection using the synthetic aperture radar (SAR) with the advantages of all day and all-weather usually monitors the significant surface change, such as flood disasters and earthquake deformation. However, when it comes to detecting subtle changes such as vehicle traces, the traditional methods ignoring the phase coherence between image pairs cannot intensify these faint changes in the difference image. The SAR coherent change detection (CCD) based on repeat-pass repeat-geometry complex images utilizing both the intensity and phase fraction could exhibit the subtle vehicle trace in the difference image. However, the complicated background and decorrelation factors significantly affect the quality of difference images, further causing great trouble for automatic trace detection. This article proposes the spatial feature enhanced Unet and adaptive data augmentation to realize vehicle trace detection. More specifically, the pseudocolor image is first synthesized based on a two-stage coherence estimation method. Then, considering the long-continuity and parallel distribution of vehicle trace samples, the enhanced Unet is constructed by fusing spatial convolutional neural network and spatial attention mechanism. After that, the adaptation data augmentation strategy is presented by introducing manual registration errors and multiple estimation windows. Finally, the experimental results on the Sandia CCD data and our measured data demonstrate the effectiveness of the proposed method.

Automated summary

This article presents a method for vehicle trace detection using synthetic aperture radar (SAR) by enhancing the Unet model and employing an adaptive data augmentation strategy.