Change Detection Using Synthetic Aperture Radar Videos
- URL: http://arxiv.org/abs/2007.14001v1
- Date: Tue, 28 Jul 2020 05:53:10 GMT
- Title: Change Detection Using Synthetic Aperture Radar Videos
- Authors: Hasara Maithree, Dilan Dinushka, Adeesha Wijayasiri
- Abstract summary: We propose an algorithm which is a combination of optical flow calculation using Lucas Kanade (LK) method and blob detection.
The developed method follows a four steps approach: image filtering and enhancement, applying LK method, blob analysis and combining LK method with blob analysis.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many researches have been carried out for change detection using temporal SAR
images. In this paper an algorithm for change detection using SAR videos has
been proposed. There are various challenges related to SAR videos such as high
level of speckle noise, rotation of SAR image frames of the video around a
particular axis due to the circular movement of airborne vehicle, non-uniform
back scattering of SAR pulses. Hence conventional change detection algorithms
used for optical videos and SAR temporal images cannot be directly utilized for
SAR videos. We propose an algorithm which is a combination of optical flow
calculation using Lucas Kanade (LK) method and blob detection. The developed
method follows a four steps approach: image filtering and enhancement, applying
LK method, blob analysis and combining LK method with blob analysis. The
performance of the developed approach was tested on SAR videos available on
Sandia National Laboratories website and SAR videos generated by a SAR
simulator.
Related papers
- Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) Algorithms [0.3124884279860061]
Anomaly detection is a key research challenge in computer vision and machine learning.
In radar imaging, specifically synthetic aperture radar (SAR), anomaly detection can be used for the classification, detection, and segmentation of objects of interest.
SARIAD provides a comprehensive suite of algorithms and datasets for assessing and developing anomaly detection approaches on SAR imagery.
arXiv Detail & Related papers (2025-04-10T20:31:25Z) - Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network.
Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
arXiv Detail & Related papers (2024-08-28T10:07:17Z) - A Beam-Segmenting Polar Format Algorithm Based on Double PCS for Video
SAR Persistent Imaging [27.6812116360734]
Video aperture radar (SAR) is attracting more attention in recent years due to its abilities of high resolution, high frame rate and advantages in continuous observation.
In the process of polar format algorithm (PFA) for spotlight mode video SAR, the wavefront curvature error (WCE) limits the imaging scene size and the 2-D images affects the efficiency.
To solve the aforementioned problems, a beam-segmenting PFA based on principle of chirp scaling (PCS), called BS-PCS-PFA, is proposed for video SAR imaging.
The proposed method can significantly expand the effective size of PFA, and the better operational efficiency
arXiv Detail & Related papers (2023-12-19T14:39:49Z) - Improved Difference Images for Change Detection Classifiers in SAR
Imagery Using Deep Learning [0.0]
This paper proposes a new method of improving SAR image processing to produce higher quality difference images for the classification algorithms.
The method is built on a neural network-based mapping transformation function that produces artificial SAR images from a location in the requested acquisition conditions.
arXiv Detail & Related papers (2023-03-31T06:57:34Z) - Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in
VIS and NIR Scenario [87.72258480670627]
Existing face forgery detection methods based on frequency domain find that the GAN forged images have obvious grid-like visual artifacts in the frequency spectrum compared to the real images.
This paper proposes a Cosine Transform-based Forgery Clue Augmentation Network (FCAN-DCT) to achieve a more comprehensive spatial-temporal feature representation.
arXiv Detail & Related papers (2022-07-05T09:27:53Z) - SAR Despeckling using a Denoising Diffusion Probabilistic Model [52.25981472415249]
The presence of speckle degrades the image quality and adversely affects the performance of SAR image understanding applications.
We introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling.
The proposed method achieves significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods.
arXiv Detail & Related papers (2022-06-09T14:00:26Z) - Autoregressive Model for Multi-Pass SAR Change Detection Based on Image
Stacks [0.0]
Change detection is an important synthetic aperture radar (SAR) application, usually used to detect changes on the ground scene measurements in different moments in time.
In this study, image stack information can be treated as a data series over time and can be modeled by autoregressive (AR) models.
Applying AR model for each pixel position in the image stack, we obtained an estimated image of the ground scene which can be used as a reference image.
arXiv Detail & Related papers (2022-06-05T21:46:11Z) - Transformer-based SAR Image Despeckling [53.99620005035804]
We introduce a transformer-based network for SAR image despeckling.
The proposed despeckling network comprises of a transformer-based encoder which allows the network to learn global dependencies between different image regions.
Experiments show that the proposed method achieves significant improvements over traditional and convolutional neural network-based despeckling methods.
arXiv Detail & Related papers (2022-01-23T20:09:01Z) - Cross-Camera Human Motion Transfer by Time Series Analysis [11.454103393879368]
We propose an algorithm that identifies motion seasonality and constructs an additive model to extract transferable patterns.
We improve pose estimation in low-resolution videos by leveraging patterns derived from HR counterparts.
arXiv Detail & Related papers (2021-09-29T03:39:01Z) - Robust Unsupervised Video Anomaly Detection by Multi-Path Frame
Prediction [61.17654438176999]
We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design.
Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
arXiv Detail & Related papers (2020-11-05T11:34:12Z) - 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) - A Modified Fourier-Mellin Approach for Source Device Identification on
Stabilized Videos [72.40789387139063]
multimedia forensic tools usually exploit characteristic noise traces left by the camera sensor on the acquired frames.
This analysis requires that the noise pattern characterizing the camera and the noise pattern extracted from video frames under analysis are geometrically aligned.
We propose to overcome this limitation by searching scaling and rotation parameters in the frequency domain.
arXiv Detail & Related papers (2020-05-20T12:06:40Z)
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.