Automatic Target Recognition Based on Rough Set-Support Vector Machine in SAR Images

Xiong, Wei; Cao, Lihua

doi:10.1109/cso.2009.27

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…However, these conventional methods are limited in their ability to handle the complex nature of SAR backscattering signals and the variability of environmental conditions, leading to reduced accuracy and robustness for ship classification in SAR images [5]. Machine learning algorithms such as support vector machines (SVM), k-nearest neighbors (KNN), and random forests (RF) have been applied to automatically classify ships in SAR images with high accuracy [6]- [8]. Some studies have also explored the use of hybrid approaches that combine multiple machine learning algorithms for ship classification, such as feature selection followed by SVM classification [9].…”

Section: Introductionmentioning

confidence: 99%

Scattering-Point Topology for Few-Shot Ship Classification in SAR Images

Zhang,

Lu,

Qiu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) has emerged as a critical technology for detecting and classifying objects such as ships in challenging environments. However, few-shot learning remains challenging due to the limited availability of labeled SAR data, complex radar backscatter, and variations in imaging parameters. In this paper, we propose a novel network, scattering point topology for few-shot ship classification (SPT-FSC), which addresses these challenges by incorporating scattering characteristics into the network learning process through a scattering point topology (SPT) based on scattering key points. We design a topology encoding branch (TEB) through a series of operations to encode the topological information of scattering points, resulting in a SPT embedding that improves the network's adaptability to the imaging mechanism and reduces imaging variability in SAR images. To effectively fuse the SPT embedding and image features extracted from a convolutional neural network (CNN), we introduce a novel mechanism named reciprocal feature fusion attention (RFFA). Additionally, to address the limited diversity in the training data, we apply fine-tuning based methodologies and construct a fine-grained ship classification dataset by combining the OpenSARShip and FUSAR-Ship datasets. Our comprehensive experiments on these datasets demonstrate the effectiveness of our proposed SPT-FSC method, achieving high accuracy and robustness in few-shot ship classification tasks for SAR images, outperforming existing methods in this domain.

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Section: Introductionmentioning

confidence: 99%

Scattering-Point Topology for Few-Shot Ship Classification in SAR Images

Zhang,

Lu,

Qiu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A very large number of ATR algorithms have been proposed in recent decades [ 3 , 4 , 6 , 7 ]. Some have been based primarily on the computation of certain types of features, such as PCA [ 8 ], edge and corner descriptors [ 9 ], wavelets [ 10 ] or deformable templates [ 11 ], while others have been driven more by a particular classification scheme, e.g., neural networks [ 12 ], support vector machines (SVM) [ 13 ] or sparse representations [ 14 ]. In the closely-related fields of computer vision and visual tracking, there have been significant developments in object detection and recognition based on visual features, including the histogram of oriented gradients (HOG) [ 15 , 16 ], the scale-invariant feature transform (SIFT) [ 17 ], spin images [ 18 ], patch features [ 19 ], shape contexts [ 20 ], optical flow [ 21 ] and local binary patterns [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

Fan

Gong

et al. 2015

Sensors

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We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation).

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Histogram-based perceptual hash algorithm for synthetic aperture radar image segmentation

Han

Wei

et al. 2018

J. Electron. Imag.

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Automatic Target Recognition Based on Rough Set-Support Vector Machine in SAR Images

Cited by 3 publications

References 6 publications

Scattering-Point Topology for Few-Shot Ship Classification in SAR Images

Scattering-Point Topology for Few-Shot Ship Classification in SAR Images

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

Histogram-based perceptual hash algorithm for synthetic aperture radar image segmentation

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