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Found 10 papers, 6 papers with code
SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTs
Comprehensive evaluations on various downstream tasks, including few-shot classification, segmentation, visualization, and pattern detection, demonstrate the effectiveness and versatility of the proposed approach.
Theory and Implementation of Complex-Valued Neural Networks
This work explains in detail the theory behind Complex-Valued Neural Network (CVNN), including Wirtinger calculus, complex backpropagation, and basic modules such as complex layers, complex activation functions, or complex weight initialization.
Impact of PolSAR pre-processing and balancing methods on complex-valued neural networks segmentation tasks
In this paper, we investigated the semantic segmentation of Polarimetric Synthetic Aperture Radar (PolSAR) using Complex-Valued Neural Network (CVNN).
Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar Imaging
Experiments are performed to show the advantages of our method compared to the conventional Reed-Xiaoli algorithm, highlighting the importance of an efficient despeckling pre-processing step.
Riemannian optimization for non-centered mixture of scaled Gaussian distributions
The proposed Riemannian gradient descent algorithm is leveraged to solve this second minimization problem.
Robust Geometric Metric Learning
This paper proposes new algorithms for the metric learning problem.
Complex-Valued vs. Real-Valued Neural Networks for Classification Perspectives: An Example on Non-Circular Data
First, we show the potential interest of Complex-Valued Neural Network (CVNN) on classification tasks for complex-valued datasets.
Automatic Target Detection for Sparse Hyperspectral Images
The sparse component is directly used for the detection, that is, the targets are simply detected at the non-zero entries of the sparse target HSI.
Target And Background Separation in Hyperspectral Imagery for Automatic Target Detection
In this paper, we propose a method for separating known targets of interests from the background in hyperspectral imagery.
Image and Video Processing Signal Processing
Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery
Given a target prior information, our goal is to propose a method for automatically separating targets of interests from the background in hyperspectral imagery.
