Using Neural Networks for Fast SAR Roughness Estimation of High Resolution Images

• URL: http://arxiv.org/abs/2309.03351v1
• Date: Wed, 6 Sep 2023 20:24:13 GMT
• Title: Using Neural Networks for Fast SAR Roughness Estimation of High Resolution Images
• Authors: Li Fan, Jeova Farias Sales Rocha Neto
• Abstract summary: We propose a neural network-based estimation framework that learns how to predict underlying parameters of $G_I0$ samples. We show that this approach leads to an estimator that is quicker, yields less estimation error and is less prone to failures.
• Score: 2.6107298043931197
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The analysis of Synthetic Aperture Radar (SAR) imagery is an important step in remote sensing applications, and it is a challenging problem due to its inherent speckle noise. One typical solution is to model the data using the $G_I^0$ distribution and extract its roughness information, which in turn can be used in posterior imaging tasks, such as segmentation, classification and interpretation. This leads to the need of quick and reliable estimation of the roughness parameter from SAR data, especially with high resolution images. Unfortunately, traditional parameter estimation procedures are slow and prone to estimation failures. In this work, we proposed a neural network-based estimation framework that first learns how to predict underlying parameters of $G_I^0$ samples and then can be used to estimate the roughness of unseen data. We show that this approach leads to an estimator that is quicker, yields less estimation error and is less prone to failures than the traditional estimation procedures for this problem, even when we use a simple network. More importantly, we show that this same methodology can be generalized to handle image inputs and, even if trained on purely synthetic data for a few seconds, is able to perform real time pixel-wise roughness estimation for high resolution real SAR imagery.

Related papers

• X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images [49.546627070454456]
  The distribution inconsistency between real and simulated data is the main obstacle that influences the utility of simulated SAR images. We propose a novel trustworthy utility evaluation framework with a counterfactual explanation for simulated SAR images for the first time, denoted as X-Fake. The proposed framework is validated on four simulated SAR image datasets obtained from electromagnetic models and generative artificial intelligence approaches.
  arXiv  Detail & Related papers  (2024-07-28T09:27:53Z)
• DepthFM: Fast Monocular Depth Estimation with Flow Matching [22.206355073676082]
  Current discriminative approaches to this problem are limited due to blurry artifacts. State-of-the-art generative methods suffer from slow sampling due to their SDE nature. We observe that this can be effectively framed using flow matching, since its straight trajectories through solution space offer efficiency and high quality.
  arXiv  Detail & Related papers  (2024-03-20T17:51:53Z)
• RANRAC: Robust Neural Scene Representations via Random Ray Consensus [12.161889666145127]
  RANdom RAy Consensus (RANRAC) is an efficient approach to eliminate the effect of inconsistent data. We formulate a fuzzy adaption of the RANSAC paradigm, enabling its application to large scale models. Results indicate significant improvements compared to state-of-the-art robust methods for novel-view synthesis.
  arXiv  Detail & Related papers  (2023-12-15T13:33:09Z)
• Match and Locate: low-frequency monocular odometry based on deep feature matching [0.65268245109828]
  We introduce a novel approach for the robotic odometry which only requires a single camera. The approach is based on matching image features between the consecutive frames of the video stream using deep feature matching models. We evaluate the performance of the approach in the AISG-SLA Visual Localisation Challenge and find that while being computationally efficient and easy to implement our method shows competitive results.
  arXiv  Detail & Related papers  (2023-11-16T17:32:58Z)
• Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring [48.80983873199214]
  We develop a data-driven approach to model the saturated pixels by a learned latent map. Based on the new model, the non-blind deblurring task can be formulated into a maximum a posterior (MAP) problem. To estimate high-quality deblurred images without amplified artifacts, we develop a prior estimation network.
  arXiv  Detail & Related papers  (2023-08-10T12:53:30Z)
• Improving Log-Cumulant Based Estimation of Roughness Information in SAR imagery [0.0]
  We propose improvements to parameter estimation in $mathcalG0$ distributions using the Method of Log-Cumulants. We show how we can use this method to achieve fast and reliable SAR image understanding based on roughness information.
  arXiv  Detail & Related papers  (2023-06-22T20:53:39Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• FasterPose: A Faster Simple Baseline for Human Pose Estimation [65.8413964785972]
  We propose a design paradigm for cost-effective network with LR representation for efficient pose estimation, named FasterPose. We study the training behavior of FasterPose, and formulate a novel regressive cross-entropy (RCE) loss function for accelerating the convergence. Compared with the previously dominant network of pose estimation, our method reduces 58% of the FLOPs and simultaneously gains 1.3% improvement of accuracy.
  arXiv  Detail & Related papers  (2021-07-07T13:39:08Z)
• Unsupervised Single Image Super-resolution Under Complex Noise [60.566471567837574]
  This paper proposes a model-based unsupervised SISR method to deal with the general SISR task with unknown degradations. The proposed method can evidently surpass the current state of the art (SotA) method (about 1dB PSNR) not only with a slighter model (0.34M vs. 2.40M) but also faster speed.
  arXiv  Detail & Related papers  (2021-07-02T11:55:40Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• Estimation of Camera Response Function using Prediction Consistency and Gradual Refinement with an Extension to Deep Learning [42.70498574189067]
  Most existing methods for CRF estimation from a single image fail to handle general real images. We introduce a non-deep-learning method using prediction consistency and gradual refinement. Our method outperforms the existing single-image methods for daytime and nighttime real images.
  arXiv  Detail & Related papers  (2020-10-08T14:19:48Z)
• SAR2SAR: a semi-supervised despeckling algorithm for SAR images [3.9490074068698]
  Deep learning algorithm with self-supervision is proposed in this paper: SAR2SAR. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle. Results on real images are discussed, to show the potential of the proposed algorithm.
  arXiv  Detail & Related papers  (2020-06-26T15:07:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.