Algorithms that correct for volume atmospheric turbulence in coherent imagery are computationally intensive, typically requiring several iterations to converge to a solution with a split-step model, where each iteration involves multiple optical propagation computations. We examine the sampling requirements for split-step modeling using phase-space optics and show that we can propagate fields accurately, using array sizes that are 2-4× smaller than the array sizes used in a typical split-step model. These smaller array sizes can be used when the aperture and field stops for the imaging system are used as intermediary planes for individual propagation steps. We evaluate the fidelity of vacuum split-step propagation results, describe split-step model adjustments needed to accommodate diffraction and turbulence effects, and illustrate how we use split-step models for analyzing the expected performance of turbulence-compensation algorithms.
We present a method for monitoring rapidly urbanizing areas with deep learning techniques. This method was generated during participation in the SpaceNet7 deep learning challenge and utilizes a U-Net architecture for semantically labeling each frame in a time series of monthly images that span roughly two years. The image sequences were collected over one hundred rapidly urbanizing regions. We discuss our network architecture and post processing algorithms for combining multiple semantically labeled frames to provide object level change detection.
Random phase errors due to atmospheric fluctuations are a ubiquitous challenge for ground based optical imaging interferometers. We present methods for dealing with these atmospheric phase errors to improve image reconstruction algorithms. The first method utilizes a scale-and-linear-phase-invariant error metric during nonlinear optimization. This method is prone to stagnation in local minima. The second method is a global linear phase correction that is applied prior to image reconstruction, either in fringe processing or as a simple preprocessing step to image reconstruction. This phase calibration method, like the baseline bootstrapping concept, is possible only with certain beam combination configurations and requires multispectral measurements. This phase correction is coarse but provides a solution within the capture range of the nonlinear optimization of the first method. Using both methods results in a simplified image reconstruction algorithm that produces a high-fidelity reconstruction.
Imaging interferometry suffers from sparse Fourier measurements, and, at the visible wavelengths, a lack of phase information, creating a need for an image reconstruction algorithm. A support constraint is useful for optimization but is often not known a priori. The two-point rule for finding an object support from the autocorrelation is limited in usefulness by the sparsity and non-uniformity of the Fourier data and is insufficient for image reconstruction. Compactness, a common prior, does not require knowledge of the support. Compactness penalizes solutions that have bright pixels away from the center, favoring soft-edged objects with a bright center and darker extremities. With regards to imaging hard-edged objects such as satellites, a support constraint is desired but unknown and compactness may be unfavorable. Combining various techniques, a method of simultaneously estimating the object’s support and the object’s intensity distribution is presented. Though all the optimization parameters are in the image domain, we are effectively performing phase retrieval at the measurement locations and interpolation between the sparse data points.
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