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Found 17 papers, 8 papers with code
Traditional to Transformers: A Survey on Current Trends and Future Prospects for Hyperspectral Image Classification
Hyperspectral image classification is a challenging task due to the high dimensionality and complex nature of hyperspectral data.
Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image Classification
This paper presents an innovative disjoint sampling approach for training SOTA models on Hyperspectral image classification (HSIC) tasks.
Pyramid Hierarchical Transformer for Hyperspectral Image Classification
The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and scalability concerns.
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
In this study, we present semi-supervised spatio-temporal anomaly detection (AD) monitoring for the physics particle reading channels of the hadronic calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM.
Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification
2D Convolutional Neural Networks (CNN) emerged as a viable network whereas, 3D CNNs are a better alternative due to accurate classification.
A Fast and Compact Hybrid CNN for Hyperspectral Imaging-based Bloodstain Classification
A potential application of bloodstain identification and classification using Hyperspectral Imaging (HSI) can be used as substance classification in forensic science for crime scene analysis.
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification
The resulting \textit{attention-fused hybrid network} (AfNet) is based on three attention-fused parallel hybrid sub-nets with different kernels in each block repeatedly using high-level features to enhance the final ground-truth maps.
3D/2D regularized CNN feature hierarchy for Hyperspectral image classification
Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels.
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images.
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset
In clusters based techniques, we achieved 96. 96%, 91. 4% and 97. 54% of classification accuracy by using RF on Flow & MQTT features, TCP features and top features from both clusters.
Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification
Traditional Active/Self/Interactive Learning for Hyperspectral Image Classification (HSIC) increases the size of the training set without considering the class scatters and randomness among the existing and new samples.
A Fast 3D CNN for Hyperspectral Image Classification
Hyperspectral imaging (HSI) has been extensively utilized for a number of real-world applications.
Efficient Data Analytics on Augmented Similarity Triplets
Data analysis require a pairwise proximity measure over objects.
Unsupervised adulterated red-chili pepper content transformation for hyperspectral classification
Preserving red-chili quality is of utmost importance in which the authorities demand the quality techniques to detect, classify and prevent it from the impurities.
Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction
Hyperspectral image analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information.
Digital Makeup from Internet Images
Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different parts in the source image.
