Search Results for author:
Found 11 papers, 6 papers with code
DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification
Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information.
Computational Efficiency
Hyperspectral Image Classification
+1
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image Classification
In this study, we propose a novel multi-stage active transfer learning (ATL) framework that integrates a Spatial-Spectral Transformer (SST) with an active learning process for efficient HSI classification.
Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
To address these challenges, we propose the morphological spatial mamba (SMM) and morphological spatial-spectral Mamba (SSMM) model (MorpMamba), which combines the strengths of morphological operations and the state space model framework, offering a more computationally efficient alternative to transformers.
Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations.
Computational Efficiency
Hyperspectral Image Classification
+1
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification
Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications.
Computational Efficiency
Hyperspectral Image Classification
+1
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.
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.
General Classification
Hyperspectral Image Classification
+1
Towards Anthropo-inspired Computational Systems: the $P^3$ Model
This paper proposes a model which aim is providing a more coherent framework for agents design.
A Cognitive Architecture for the Implementation of Emotions in Computing Systems
In this paper we present a new neurobiologically-inspired affective cognitive architecture: NEUCOGAR (NEUromodulating COGnitive ARchitecture).
