RETRACTED ARTICLE: FPGA-based reflection image removal using cognitive neural networks

Saptalakar, Bairu K.; Latte, Mrityunjaya V.

doi:10.1007/s13204-022-02352-6

Cited by 2 publications

(2 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This mechanism combines information from both the spectral and spatial (SaS) dimensions of the HSI, allowing the model to adaptively assign weights to various locations based on their discriminative capability. Saptalakar and Latte introduced a model that can learn to balance the contribution of SaS features for each class, mitigating the impact of class imbalances [16]. This approach promotes a more comprehensive understanding of the data and facilitates improved generalization across all classes, even those with limited training samples.…”

Section: Introductionmentioning

confidence: 99%

“…TWk represents the tile size for the output features loop. The following constraints can be formed using the relationship between the input and output variable as, 𝑊 𝑖 = 𝑆(𝑊 𝑜 − 1) + 𝑊 𝑘 𝐻 𝑖 = 𝑆(𝐻 𝑜 − 1) + 𝐻 𝑘(15) 𝑇𝐻 𝑖 = 𝑆(𝑇𝐻 𝑜 − 1) + 𝑇𝐻 𝑘 𝑇𝑊 𝑖 = 𝑆(𝑇𝑊 𝑜 − 1) + 𝑇𝑊 𝑘(16)…”

mentioning

confidence: 99%

See 1 more Smart Citation

Graph Neural Networks with Autoregressive Moving Average Graph Filter and Graph- Regularized Sparse Coding for Accurate Hyperspectral Image Classification on FPGA

Muralikrishnan,

2024

Preprint

View full text Add to dashboard Cite

Classification techniques based on hyperspectral images (HSIs) have gained significant importance in target identification, mineral mapping, and environmental management due to rapid advancements in hyperspectral remote sensing technology. Graph Neural Networks (GNNs) have become a key technique, attracting considerable attention in HSI analysis. However, GNN-based techniques frequently depend on a graph filter to extract the intricate spectral-spatial characteristics inherent in HSI data, which limits the ability to fully exploit the diverse and rich information available. This can lead to less comprehensive feature representation. To overcome these limitations, we propose a novel method called AMAGC-GNN, Autoregressive Moving Average Graph Filter and Graph-Regularized Sparse Coding for the GNN. The ARMA graph filters play a crucial role in spectral filtering, effectively capturing and preserving complex spectral relationships while mitigating the common oversmoothing problem in traditional GNNs, ensuring node features retain their uniqueness and discriminative power. Concurrently, graph-regularized sparse coding transforms the input HSI data into sparse coefficients, representing the most relevant features efficiently. This combined approach leverages the strengths of both techniques, providing adaptive and precise spectral filtering and compact, discriminative feature representation. AMAGC-GNN outperforms other techniques, achieving significant improvements in Cohen's Kappa coefficient (6.58% and 6.15%), per-class accuracy (6.24% and 5.57%), and overall accuracy (6.24% and 6.30%) across Kennedy Space Centre and Indian pines datasets. Furthermore, our implementation of AMAGF-GNN on Virtex-7 field-programmable gate arrays (FPGAs) demonstrates promising results for real-world applications in HSI classification, particularly in achieving highly accurate target localization.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Graph Neural Networks with Autoregressive Moving Average Graph Filter and Graph- Regularized Sparse Coding for Accurate Hyperspectral Image Classification on FPGA

Muralikrishnan,

2024

Preprint

View full text Add to dashboard Cite

show abstract

Enhancing hyperspectral image classification with graph attention neural network

Rathakrishnan,

Raja

2024

J. Electron. Imag.

View full text Add to dashboard Cite

Due to the rapid advancement of hyperspectral remote sensing technology, classification methods based on hyperspectral images (HSIs) have gained increasing significance in the processes of target identification, mineral mapping, and environmental management. This importance arises from the fact that HSIs offer a more comprehensive understanding of a target's composition. However, addressing the challenges posed by the high dimensionality and redundancy of HSI sets, coupled with potential class imbalances in hyperspectral datasets, remains a complex task. Both convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have demonstrated promising results in HSI classification in recent years. Nonetheless, CNNs struggle to attain high accuracy with limited sample sizes, whereas GCNs demand substantial computational resources. Oversmoothing remains a persistent challenge with conventional GCNs. In response to these issues, an approach known as the graph attention neural network for remote target classification (GATN-RTC) has been proposed. GATN-RTC employs a spectral filter and an autoregressive moving average filter to classify distant targets, addressing datasets both with and without labeled samples. To evaluate the performance of GATN-RTC, we conducted a comparative analysis against state-of-the-art methodologies using key performance metrics, such as overall accuracy (OA), per-class accuracy, and the Cohen's Kappa statistic (KC). The findings reveal that GATN-RTC outperforms existing approaches, achieving improvements of 5.95% in OA, 5.33% in per-class accuracy, and 8.28% in the Cohen's KC for the Salinas dataset. Furthermore, it demonstrates enhancements of 6.05% and 6.4% in OA, 6.56% and 5.89% in per-class accuracy, and 6.71% and 6.23% in the Cohen's KC for the Pavia University dataset.

show abstract

RETRACTED ARTICLE: FPGA-based reflection image removal using cognitive neural networks

Cited by 2 publications

References 50 publications

Graph Neural Networks with Autoregressive Moving Average Graph Filter and Graph- Regularized Sparse Coding for Accurate Hyperspectral Image Classification on FPGA

Graph Neural Networks with Autoregressive Moving Average Graph Filter and Graph- Regularized Sparse Coding for Accurate Hyperspectral Image Classification on FPGA

Enhancing hyperspectral image classification with graph attention neural network

Contact Info

Product

Resources

About