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SAR Despeckling via Region-Aware Sparse Representation and Statistical Noise Approximation
Xuran Hu, Mingzhe Zhu, Djordje Stanković +3
Synthetic Aperture Radar (SAR) imagery are widely utilized in remote sensing due to their all-weather, all-day imaging capabilities. However, SAR images are highly susceptible to n…
Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks
Xuran Hu, Mingzhe Zhu, Zhenpeng Feng +2
The inherent "black box" nature of deep neural networks (DNNs) compromises their transparency and reliability. Recently, explainable AI (XAI) has garnered increasing attention from…
Multi-task SAR Image Processing via GAN-based Unsupervised Manipulation
Xuran Hu, Mingzhe Zhu, Ziqiang Xu +2
Generative Adversarial Networks (GANs) have shown tremendous potential in synthesizing a large number of realistic SAR images by learning patterns in the data distribution. Some GA…
Manifold-based Shapley for SAR Recognization Network Explanation
Xuran Hu, Mingzhe Zhu, Yuanjing Liu +2
Explainable artificial intelligence (XAI) holds immense significance in enhancing the deep neural network's transparency and credibility, particularly in some risky and high-cost s…
SAR Despeckling via Regional Denoising Diffusion Probabilistic Model
Xuran Hu, Ziqiang Xu, Zhihan Chen +3
Speckle noise poses a significant challenge in maintaining the quality of synthetic aperture radar (SAR) images, so SAR despeckling techniques have drawn increasing attention. Desp…