Interferometric phase unwrapping is one of the most challenging research topics for the remote sensing community. Recovering and correctly estimating the true interferometric phase signal from the received wrapped one provides critical information about changes in the Earth’s surface over time. Interferometric synthetic aperture radar (InSAR) has been widely used to extract such displacement estimates. However, InSAR images are affected often by a particular type of noise known as Gaussian. The presence of Gaussian noise in InSAR data can make the phase unwrapping process more difficult. In this paper, we introduce a convolutional deep learning-based network to perform simultaneous interferometric phase denoising and unwrapping. Quantitative and qualitative evaluations, made on synthetic and real world InSAR data, show that the proposed approach is able to produce accurate results even in the presence of strong noise.
Very high resolution satellite images can be used to generate stereoscopic digital elevation models (DEMs), efficiently and at scale, as exemplified by the upcoming CO3D mission, which aims to produce worldwide DEMs by the end of 2025. In this paper we present a deep learning stereo-vision algorithm, integrated in the Stereo Pipeline for Pushbroom Images (S2P) framework. The proposed stereo matching method applies a Siamese convolutional neural network (CNN) to construct a cost volume. A median filter is applied to every slice in the cost volume to enforce spatial smoothness, and another CNN estimates a confidence map which is used to derive the final disparity map. Simulation results on the IARPA dataset show that the proposed method improves completeness by 4.5%, compared to the state of the art. A qualitative assessment also shows that the proposed method generates DEMs with less noise.
Satellite imagery provides information crucial for remote sensing applications. However, the images themselves can suffer from systematic and random artefacts which reduce the utility and accuracy of datasets. In particular, radiometric miscalibration due to temporal variation of the detector response may result in stripe noise. We report a method for suppressing striping in remote sensing images by use of a Fourier filter shaped like a superGaussian function. In comparison to both established ‘traditional’ and deep-learning-based destriping techniques, our method demonstrates superior destriping performance for both remote sensing images with native striping as well as those with stripes added to them. Our method simultaneously meets the three criteria of fidelity, speed and flexibility, enabling an efficient improvement in the radiometric accuracy of images from a wide range of satellite sources.
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