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Found 10 papers, 3 papers with code
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation
The recognition performance of 94. 18\% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results.
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
In this letter, we propose an entropy-awareness meta-learning method that improves the exclusiveness of feature distribution of known classes which means our method is effective for not only classifying the seen classes but also encountering the unseen other classes.
SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier
However, the characteristics of SAR ship images, large inner-class variance, and small interclass difference lead to the whole features containing useless partial features and a single feature center for each class in the classifier failing with large inner-class variance.
SAR Ship Target Recognition Via Multi-Scale Feature Attention and Adaptive-Weighed Classifier
Therefore, we proposed a SAR ship recognition method via multi-scale feature attention and adaptive-weighted classifier to enhance features in each scale, and adaptively choose the effective feature scale for accurate recognition.
SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner
The designed augmenter increases the amount of information available for supervised training and improves the separability of the extracted features.
Crucial Feature Capture and Discrimination for Limited Training Data SAR ATR
Based on the initial recognition results, the feature capture module automatically searches and locks the crucial image regions for correct recognition, which we named as the golden key of image.
Causal SAR ATR with Limited Data via Dual Invariance
As a result, it has a detrimental causal effect damaging the efficacy of feature $X$ extracted from SAR images, leading to weak generalization of SAR ATR with limited data.
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data
Then, a feature discrimination approach with hybrid similarity measurement is introduced to measure and mitigate the structural and vector angle impacts of varying imaging conditions on the extracted features from SAR images.
SAR ATR under Limited Training Data Via MobileNetV3
In recent years, deep learning has been widely used to solve the bottleneck problem of synthetic aperture radar (SAR) automatic target recognition (ATR).
Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK
We develop a distribution regression model under endogenous sample selection.
