Brain-inspired remote sensing interpretation: A comprehensive survey
Brain-inspired algorithms have become a new trend in next-generation artificial intelligence.
Through research on brain science, the intelligence of remote sensing algorithms can be …
Through research on brain science, the intelligence of remote sensing algorithms can be …
HAR-DeepConvLG: Hybrid deep learning-based model for human activity recognition in IoT applications
W Ding, M Abdel-Basset, R Mohamed - Information Sciences, 2023 - Elsevier
Smartphones and wearable devices have built-in sensors that can collect multivariant time-
series data that can be used to recognize human activities. Research on human activity …
series data that can be used to recognize human activities. Research on human activity …
Open set recognition with incremental learning for SAR target classification
X Ma, K Ji, S Feng, L Zhang, B **ong… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) target classification is an important application in SAR image
interpretation. In practical applications, the battlefield is open and dynamic, and the SAR …
interpretation. In practical applications, the battlefield is open and dynamic, and the SAR …
LIME-assisted automatic target recognition with SAR images: Toward incremental learning and explainability
Integrating an automatic target recognition (ATR) system into real-world applications
presents a challenge as it may frequently encounter new samples from unseen classes. To …
presents a challenge as it may frequently encounter new samples from unseen classes. To …
Threshold-free open-set learning network for SAR automatic target recognition
Many advanced automatic target recognition (ATR) methods for synthetic aperture radar
(SAR) encounter limitations, as they heavily rely on the assumption of a closed-set …
(SAR) encounter limitations, as they heavily rely on the assumption of a closed-set …
SAR target open-set recognition based on joint training of class-specific sub-dictionary learning
X Ma, K Ji, L Zhang, S Feng, B **ong… - IEEE Geoscience and …, 2023 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) automatic target recognition (ATR) has attracted extensive
attention and achieved satisfactory results. However, most SAR ATR methods follow the …
attention and achieved satisfactory results. However, most SAR ATR methods follow the …
[HTML][HTML] Proportional similarity-based Openmax classifier for open set recognition in SAR images
Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the “closed
world” assumption, ie, they only work with what was previously observed. Nevertheless, the …
world” assumption, ie, they only work with what was previously observed. Nevertheless, the …
Statistical hypothesis testing based on machine learning: Large deviations analysis
We study the performance of Machine Learning (ML) classification techniques. Leveraging
the theory of large deviations, we provide the mathematical conditions for a ML classifier to …
the theory of large deviations, we provide the mathematical conditions for a ML classifier to …
Multimodal discriminative feature learning for SAR ATR: A fusion framework of phase history, scattering topology, and image
Z Wen, Y Yu, Q Wu - IEEE Transactions on Geoscience and …, 2023 - ieeexplore.ieee.org
Deep learning has emerged as the dominant paradigm for synthetic aperture radar (SAR)
automatic target recognition (ATR), which induces discriminative visual features from target …
automatic target recognition (ATR), which induces discriminative visual features from target …
An open set recognition for sar targets based on encoding-conditional decoding network with reject threshold adaptation
Current synthetic aperture radar (SAR) automatic target recognition (ATR) methods primarily
focus on closed set recognition, ie, they typically assume that the training set covers all …
focus on closed set recognition, ie, they typically assume that the training set covers all …