PeaceGAN: A GAN-based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier

• URL: http://arxiv.org/abs/2103.15469v1
• Date: Mon, 29 Mar 2021 10:03:09 GMT
• Title: PeaceGAN: A GAN-based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier
• Authors: Jihyong Oh, Munchurl Kim
• Abstract summary: We propose a novel GAN-based multi-task learning (MTL) method for SAR target image generation, called PeaceGAN. PeaceGAN uses both pose angle and target class information, which makes it possible to produce SAR target images of desired target classes at intended pose angles.
• Score: 50.17500790309477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although Generative Adversarial Networks (GANs) are successfully applied to diverse fields, training GANs on synthetic aperture radar (SAR) data is a challenging task mostly due to speckle noise. On the one hands, in a learning perspective of human's perception, it is natural to learn a task by using various information from multiple sources. However, in the previous GAN works on SAR target image generation, the information on target classes has only been used. Due to the backscattering characteristics of SAR image signals, the shapes and structures of SAR target images are strongly dependent on their pose angles. Nevertheless, the pose angle information has not been incorporated into such generative models for SAR target images. In this paper, we firstly propose a novel GAN-based multi-task learning (MTL) method for SAR target image generation, called PeaceGAN that uses both pose angle and target class information, which makes it possible to produce SAR target images of desired target classes at intended pose angles. For this, the PeaceGAN has two additional structures, a pose estimator and an auxiliary classifier, at the side of its discriminator to combine the pose and class information more efficiently. In addition, the PeaceGAN is jointly learned in an end-to-end manner as MTL with both pose angle and target class information, thus enhancing the diversity and quality of generated SAR target images The extensive experiments show that taking an advantage of both pose angle and target class learning by the proposed pose estimator and auxiliary classifier can help the PeaceGAN's generator effectively learn the distributions of SAR target images in the MTL framework, so that it can better generate the SAR target images more flexibly and faithfully at intended pose angles for desired target classes compared to the recent state-of-the-art methods.

Related papers

• SARATR-X: A Foundation Model for Synthetic Aperture Radar Images Target Recognition [19.776275680586977]
  This paper aims to achieve general SAR ATR based on a foundation model with Self-Supervised Learning (SSL) A foundation model named SARATR-X is proposed with the following four aspects. First, we integrated 14 datasets with various target categories and imaging conditions as a pre-training dataset. Second, different model backbones were discussed to find the most suitable approaches for remote-sensing images. Third, we applied two-stage training and SAR gradient features to ensure the diversity and scalability of SARATR-X.
  arXiv  Detail & Related papers  (2024-05-15T14:17:44Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture [23.375515181854254]
  This study investigates an effective Self-Supervised Learning (SSL) method for SAR Automatic Target Recognition (ATR) SSL aims to construct supervision signals directly from the data, which minimizes the need for expensive expert annotation. We present a novel Joint-Embedding Predictive Architecture for SAR ATR (SAR-JEPA), which leverages local masked patches to predict the multi-scale SAR gradient representations of unseen context.
  arXiv  Detail & Related papers  (2023-11-26T01:05:55Z)
• CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations [90.50864830038202]
  We present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images. CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.
  arXiv  Detail & Related papers  (2023-05-01T23:11:18Z)
• De-coupling and De-positioning Dense Self-supervised Learning [65.56679416475943]
  Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects. We show that they suffer from coupling and positional bias, which arise from the receptive field increasing with layer depth and zero-padding. We demonstrate the benefits of our method on COCO and on a new challenging benchmark, OpenImage-MINI, for object classification, semantic segmentation, and object detection.
  arXiv  Detail & Related papers  (2023-03-29T18:07:25Z)
• Heterogeneous Feature Distillation Network for SAR Image Semantic Segmentation [0.0]
  We investigate how to introduce EO features to assist the training of a SAR-segmentation model. We propose a heterogeneous feature distillation network for segmenting SAR images, called HFD-Net. The proposed HFD-Net outperforms seven state-of-the-art SAR image semantic segmentation methods.
  arXiv  Detail & Related papers  (2022-10-17T12:12:45Z)
• A Dual-fusion Semantic Segmentation Framework With GAN For SAR Images [10.147351262526282]
  A network based on the widely used encoderdecoder architecture is proposed to accomplish the synthetic aperture radar (SAR) images segmentation. With the better representation capability of optical images, we propose to enrich SAR images with generated optical images via the generative adversative network (GAN) trained by numerous SAR and optical images.
  arXiv  Detail & Related papers  (2022-06-02T15:22:29Z)
• AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation [86.44683367028914]
  Aerial imagery segmentation has some unique challenges, the most critical one among which lies in foreground-background imbalance. We propose Adaptive Focus Framework (AF$), which adopts a hierarchical segmentation procedure and focuses on adaptively utilizing multi-scale representations. AF$ has significantly improved the accuracy on three widely used aerial benchmarks, as fast as the mainstream method.
  arXiv  Detail & Related papers  (2022-02-18T10:14:45Z)
• Memory-augmented Deep Unfolding Network for Guided Image Super-resolution [67.83489239124557]
  Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image. Previous model-based methods mainly takes the entire image as a whole, and assume the prior distribution between the HR target image and the HR guidance image. We propose a maximal a posterior (MAP) estimation model for GISR with two types of prior on the HR target image.
  arXiv  Detail & Related papers  (2022-02-12T15:37:13Z)
• Sparse Signal Models for Data Augmentation in Deep Learning ATR [0.8999056386710496]
  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.
  arXiv  Detail & Related papers  (2020-12-16T21:46:33Z)

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.