Sparse Signal Models for Data Augmentation in Deep Learning ATR
- URL: http://arxiv.org/abs/2012.09284v1
- Date: Wed, 16 Dec 2020 21:46:33 GMT
- Title: Sparse Signal Models for Data Augmentation in Deep Learning ATR
- Authors: Tushar Agarwal, Nithin Sugavanam and Emre Ertin
- Abstract summary: We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm.
We exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting.
- Score: 0.8999056386710496
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automatic Target Recognition (ATR) algorithms classify a given Synthetic
Aperture Radar (SAR) image into one of the known target classes using a set of
training images available for each class. Recently, learning methods have shown
to achieve state-of-the-art classification accuracy if abundant training data
is available, sampled uniformly over the classes, and their poses. In this
paper, we consider the task of ATR with a limited set of training images. We
propose a data augmentation approach to incorporate domain knowledge and
improve the generalization power of a data-intensive learning algorithm, such
as a Convolutional neural network (CNN). The proposed data augmentation method
employs a limited persistence sparse modeling approach, capitalizing on
commonly observed characteristics of wide-angle synthetic aperture radar (SAR)
imagery. Specifically, we exploit the sparsity of the scattering centers in the
spatial domain and the smoothly-varying structure of the scattering
coefficients in the azimuthal domain to solve the ill-posed problem of
over-parametrized model fitting. Using this estimated model, we synthesize new
images at poses and sub-pixel translations not available in the given data to
augment CNN's training data. The experimental results show that for the
training data starved region, the proposed method provides a significant gain
in the resulting ATR algorithm's generalization performance.
Related papers
- 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) - Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate.
We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data.
Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
arXiv Detail & Related papers (2024-03-23T22:32:06Z) - SatDM: Synthesizing Realistic Satellite Image with Semantic Layout
Conditioning using Diffusion Models [0.0]
Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant promise in synthesizing realistic images from semantic layouts.
In this paper, a conditional DDPM model capable of taking a semantic map and generating high-quality, diverse, and correspondingly accurate satellite images is implemented.
The effectiveness of our proposed model is validated using a meticulously labeled dataset introduced within the context of this study.
arXiv Detail & Related papers (2023-09-28T19:39:13Z) - Consistency Regularization for Generalizable Source-free Domain
Adaptation [62.654883736925456]
Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset.
Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets.
We propose a consistency regularization framework to develop a more generalizable SFDA method.
arXiv Detail & Related papers (2023-08-03T07:45:53Z) - Deep face recognition with clustering based domain adaptation [57.29464116557734]
We propose a new clustering-based domain adaptation method designed for face recognition task in which the source and target domain do not share any classes.
Our method effectively learns the discriminative target feature by aligning the feature domain globally, and, at the meantime, distinguishing the target clusters locally.
arXiv Detail & Related papers (2022-05-27T12:29:11Z) - Adaptive Convolutional Dictionary Network for CT Metal Artifact
Reduction [62.691996239590125]
We propose an adaptive convolutional dictionary network (ACDNet) for metal artifact reduction.
Our ACDNet can automatically learn the prior for artifact-free CT images via training data and adaptively adjust the representation kernels for each input CT image.
Our method inherits the clear interpretability of model-based methods and maintains the powerful representation ability of learning-based methods.
arXiv Detail & Related papers (2022-05-16T06:49:36Z) - Graph-based Active Learning for Semi-supervised Classification of SAR
Data [8.92985438874948]
We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods.
CNNVAE feature embedding and graph construction requires no labeled data, which reduces overfitting.
The method easily incorporates a human-in-the-loop for active learning in the data-labeling process.
arXiv Detail & Related papers (2022-03-31T00:14:06Z) - Toward Data-Driven STAP Radar [23.333816677794115]
We characterize our data-driven approach to space-time adaptive processing (STAP) radar.
We generate a rich example dataset of received radar signals by randomly placing targets of variable strengths in a predetermined region.
For each data sample within this region, we generate heatmap tensors in range, azimuth, and elevation of the output power of a beamformer.
In an airborne scenario, the moving radar creates a sequence of these time-indexed image stacks, resembling a video.
arXiv Detail & Related papers (2022-01-26T02:28:13Z) - GAN-Supervised Dense Visual Alignment [95.37027391102684]
We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end.
Inspired by the classic Congealing method, our GANgealing algorithm trains a Spatial Transformer to map random samples from a GAN trained on unaligned data to a common, jointly-learned target mode.
arXiv Detail & Related papers (2021-12-09T18:59:58Z) - A Feature Fusion-Net Using Deep Spatial Context Encoder and
Nonstationary Joint Statistical Model for High Resolution SAR Image
Classification [10.152675581771113]
A novel end-to-end supervised classification method is proposed for HR SAR images.
To extract more effective spatial features, a new deep spatial context encoder network (DSCEN) is proposed.
To enhance the diversity of statistics, the nonstationary joint statistical model (NS-JSM) is adopted to form the global statistical features.
arXiv Detail & Related papers (2021-05-11T06:20:14Z) - Generative Zero-shot Network Quantization [41.75769117366117]
Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration.
We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data.
arXiv Detail & Related papers (2021-01-21T04:10:04Z)
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.