Accelerating the inference speed of CNNs is critical to their deployment in real-world applications. Among all the pruning approaches, those implementing a sparsity learning framework have shown to be effective as the...
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Gaofen-3 (GF-3) is the first Chinese multichannel synthetic aperture radar (SAR) sensor that can operate in the dual receive channel (DRC) mode. Different from the traditional single-channel SAR system, the multichann...
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GaoFen-3(GF-3) is China's first multi-mode c-band radar imaging satellite launched on August 10, 2016. In order to meet the requirements for global ocean observing and wave detection, three wide swath modes were d...
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Multiaspect SAR has capability of providing high resolution image of static scene due to its long aperture feature. However, moving target can generate long and complex image trace in Multiaspect SAR, which may hamper...
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Visual tracking can be easily disturbed by similar surrounding objects. Such objects as hard distractors, even though being the minority among negative samples, increase the risk of target drift and model corruption, ...
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Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versatile Video Coding (VVC) in both PSNR a...
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Stereo matching, an essential step in 3D reconstruction, still faces unignorable problems due to the very high resolution and complex structures of remote sensing images. Especially in occluded areas of high buildings...
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ISBN:
(数字)9781728163741
ISBN:
(纸本)9781728163758
Stereo matching, an essential step in 3D reconstruction, still faces unignorable problems due to the very high resolution and complex structures of remote sensing images. Especially in occluded areas of high buildings and untextured areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we propose a novel method based on the pyramid stereo matching network to solve the aforementioned problems. Inspired by the classical optical flow estimation framework, we adopt the forward-backward consistency assumption to improve the accuracy. Moreover, we improve the construction of cost volume since the traditional deep-learning networks only work well for positive disparities and the disparity ranges in remote sensing images vary a lot. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two baselines in terms of average endpoint error (EPE) and the fraction of erroneous pixels(D1), and the improvements in occluded areas are significant.
Dear editor,Sparse signal processing offers a framework for synthetic aperture radar (SAR) imaging [1, 2]. As an efficient tool in sparse signal processing, L1minimization is often used in the reconstruction of SAR ...
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Dear editor,Sparse signal processing offers a framework for synthetic aperture radar (SAR) imaging [1, 2]. As an efficient tool in sparse signal processing, L1minimization is often used in the reconstruction of SAR images. When implemented in SAR imaging [3–5], L1minimization offers significant improvement in the properties by suppressing the sidelobes and clutter. However, L1minimization is known to be a biased estimator. The L1minimization based algorithms such as the iterative
Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications. This is partly due to the transparency associated with the ...
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In the tile-based 360-degree video streaming, predicting user’s future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user’s quality of experience (QoE). Traditional single-...
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