EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification

Hyperspectral imaging (HSI) provides rich spectral-spatial information across hundreds of contiguous bands, enabling precise material discrimination in applications such as environmental monitoring, agriculture, and urban analysis. However, the high dimensionality and spectral variability of HSI data pose significant challenges for feature extraction and classification. This paper presents EnergyFormer, a transformer-based framework designed to address these challenges through three key innovations: (1) Multi-Head Energy Attention (MHEA), which optimizes an energy function to selectively enhance critical spectral-spatial features, improving feature discrimination; (2) Fourier Position Embedding (FoPE), which adaptively encodes spectral and spatial dependencies to reinforce long-range interactions; and (3) Enhanced Convolutional Block Attention Module (ECBAM), which selectively amplifies informative wavelength bands and spatial structures, enhancing representation learning. Extensive experiments on the WHU-Hi-HanChuan, Salinas, and Pavia University datasets demonstrate that EnergyFormer achieves exceptional overall accuracies of 99.28\%, 98.63\%, and 98.72\%, respectively, outperforming state-of-the-art CNN, transformer, and Mamba-based models. The source code will be made available at https://github.com/mahmad000.

Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification

Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. However, it faces significant challenges due to the high-dimensional nature of the data and the complex spectral-spatial relationships inherent in HSI. Traditional methods, including conventional machine learning and convolutional neural networks (CNNs), often struggle to effectively capture these intricate spectral-spatial features and global contextual information. Transformer-based models, while powerful in capturing long-range dependencies, often demand substantial computational resources, posing challenges in scenarios where labeled datasets are limited, as is commonly seen in HSI applications. To overcome these challenges, this work proposes GraphMamba, a hybrid model that combines spectral-spatial token generation, graph-based token prioritization, and cross-attention mechanisms. The model introduces a novel hybridization of state-space modeling and Gated Recurrent Units (GRU), capturing both linear and nonlinear spatial-spectral dynamics. GraphMamba enhances the ability to model complex spatial-spectral relationships while maintaining scalability and computational efficiency across diverse HSI datasets. Through comprehensive experiments, we demonstrate that GraphMamba outperforms existing state-of-the-art models, offering a scalable and robust solution for complex HSI classification tasks.

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. In this work, we propose the Differential Spatial-Spectral Transformer (DiffFormer), a novel framework designed to address the inherent challenges of HSIC, such as spectral redundancy and spatial discontinuity. The DiffFormer leverages a Differential Multi-Head Self-Attention (DMHSA) mechanism, which enhances local feature discrimination by introducing differential attention to accentuate subtle variations across neighboring spectral-spatial patches. The architecture integrates Spectral-Spatial Tokenization through three-dimensional (3D) convolution-based patch embeddings, positional encoding, and a stack of transformer layers equipped with the SWiGLU activation function for efficient feature extraction (SwiGLU is a variant of the Gated Linear Unit (GLU) activation function). A token-based classification head further ensures robust representation learning, enabling precise labeling of hyperspectral pixels. Extensive experiments on benchmark hyperspectral datasets demonstrate the superiority of DiffFormer in terms of classification accuracy, computational efficiency, and generalizability, compared to existing state-of-the-art (SOTA) methods. In addition, this work provides a detailed analysis of computational complexity, showcasing the scalability of the model for large-scale remote sensing applications. The source code will be made available at \url{https://github.com/mahmad000/DiffFormer} after the first round of revision.

Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image Classification

The classification of hyperspectral images (HSI) is a challenging task due to the high spectral dimensionality and limited labeled data typically available for training. 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. Our approach leverages a pre-trained (initially trained) SST model, fine-tuned iteratively on newly acquired labeled samples using an uncertainty-diversity (Spatial-Spectral Neighborhood Diversity) querying mechanism. This mechanism identifies the most informative and diverse samples, thereby optimizing the transfer learning process to reduce both labeling costs and model uncertainty. We further introduce a dynamic freezing strategy, selectively freezing layers of the SST model to minimize computational overhead while maintaining adaptability to spectral variations in new data. One of the key innovations in our work is the self-calibration of spectral and spatial attention weights, achieved through uncertainty-guided active learning. This not only enhances the model's robustness in handling dynamic and disjoint spectral profiles but also improves generalization across multiple HSI datasets. Additionally, we present a diversity-promoting sampling strategy that ensures the selected samples span distinct spectral regions, preventing overfitting to particular spectral classes. Experiments on benchmark HSI datasets demonstrate that the SST-ATL framework significantly outperforms existing CNN and SST-based methods, offering superior accuracy, efficiency, and computational performance. The source code can be accessed at \url{https://github.com/mahmad000/ATL-SST}.

Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification

Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook rich spectral information in HSIs and struggle with high dimensionality and sequential data. To address these issues, we propose the SSM with multi-head self-attention and token enhancement (MHSSMamba). This model integrates spectral and spatial information by enhancing spectral tokens and using multi-head attention to capture complex relationships between spectral bands and spatial locations. It also manages long-range dependencies and the sequential nature of HSI data, preserving contextual information across spectral bands. MHSSMamba achieved remarkable classification accuracies of 97.62\% on Pavia University, 96.92\% on the University of Houston, 96.85\% on Salinas, and 99.49\% on Wuhan-longKou datasets.

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. Despite the advancements in Deep Learning (DL) and Transformer architectures for HSI Classification (HSIC), challenges such as computational efficiency and the need for extensive labeled data persist. This paper introduces WaveMamba, a novel approach that integrates wavelet transformation with the Spatial-Spectral Mamba architecture to enhance HSIC. WaveMamba captures both local texture patterns and global contextual relationships in an end-to-end trainable model. The Wavelet-based enhanced features are then processed through the state-space architecture to model spatial-spectral relationships and temporal dependencies. The experimental results indicate that WaveMamba surpasses existing models, achieving an accuracy improvement of 4.5\% on the University of Houston dataset and a 2.0\% increase on the Pavia University dataset. These findings validate its effectiveness in addressing the complex data interactions inherent in HSIs.

Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification

In recent years, Transformers have garnered significant attention for Hyperspectral Image Classification (HSIC) due to their self-attention mechanism, which provides strong classification performance. However, these models face major challenges in computational efficiency, as their complexity increases quadratically with the sequence length. The Mamba architecture, leveraging a State Space Model, offers a more efficient alternative to Transformers. This paper introduces the Spatial-Spectral Morphological Mamba (MorpMamba) model. In the MorpMamba model, a token generation module first converts the Hyperspectral Image (HSI) patch into spatial-spectral tokens. These tokens are then processed by a morphology block, which computes structural and shape information using depthwise separable convolutional operations. The extracted information is enhanced in a feature enhancement module that adjusts the spatial and spectral tokens based on the center region of the HSI sample, allowing for effective information fusion within each block. Subsequently, the tokens are refined in a multi-head self-attention block to further improve the feature space. Finally, the combined information is fed into the state space block for classification and the creation of the ground truth map. Experiments on widely used Hyperspectral (HS) datasets demonstrate that the MorpMamba model outperforms (parametric efficiency) both CNN and Transformer models.

A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers

Hyperspectral Image Classification (HSC) is a challenging task due to the high dimensionality and complex nature of Hyperspectral (HS) data. Traditional Machine Learning approaches while effective, face challenges in real-world data due to varying optimal feature sets, subjectivity in human-driven design, biases, and limitations. Traditional approaches encounter the curse of dimensionality, struggle with feature selection and extraction, lack spatial information consideration, exhibit limited robustness to noise, face scalability issues, and may not adapt well to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as powerful tools for addressing these challenges. This survey provides a comprehensive overview of the current trends and future prospects in HSC, focusing on the advancements from DL models to the emerging use of Transformers. We review the key concepts, methodologies, and state-of-the-art approaches in DL for HSC. We explore the potential of Transformer-based models in HSC, outlining their benefits and challenges. We also delve into emerging trends in HSC, as well as thorough discussions on Explainable AI and Interoperability concepts along with Diffusion Models (image denoising, feature extraction, and image fusion). Lastly, we address several open challenges and research questions pertinent to HSC. Comprehensive experimental results have been undertaken using three HS datasets to verify the efficacy of various conventional DL models and Transformers. Finally, we outline future research directions and potential applications that can further enhance the accuracy and efficiency of HSC. The Source code is available at \href{https://github.com/mahmad00/Conventional-to-Transformer-for-Hyperspectral-Image-Classification-Survey-2024}{github.com/mahmad00}.

Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification

3D Swin Transformer (3D-ST) known for its hierarchical attention and window-based processing, excels in capturing intricate spatial relationships within images. Spatial-spectral Transformer (SST), meanwhile, specializes in modeling long-range dependencies through self-attention mechanisms. Therefore, this paper introduces a novel method: an attentional fusion of these two transformers to significantly enhance the classification performance of Hyperspectral Images (HSIs). What sets this approach apart is its emphasis on the integration of attentional mechanisms from both architectures. This integration not only refines the modeling of spatial and spectral information but also contributes to achieving more precise and accurate classification results. The experimentation and evaluation of benchmark HSI datasets underscore the importance of employing disjoint training, validation, and test samples. The results demonstrate the effectiveness of the fusion approach, showcasing its superiority over traditional methods and individual transformers. Incorporating disjoint samples enhances the robustness and reliability of the proposed methodology, emphasizing its potential for advancing hyperspectral image classification.

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. In recent years, deep learning techniques have emerged as powerful tools for addressing these challenges. This survey provides a comprehensive overview of the current trends and future prospects in hyperspectral image classification, focusing on the advancements from deep learning models to the emerging use of transformers. We review the key concepts, methodologies, and state-of-the-art approaches in deep learning for hyperspectral image classification. Additionally, we discuss the potential of transformer-based models in this field and highlight the advantages and challenges associated with these approaches. Comprehensive experimental results have been undertaken using three Hyperspectral datasets to verify the efficacy of various conventional deep-learning models and Transformers. Finally, we outline future research directions and potential applications that can further enhance the accuracy and efficiency of hyperspectral image classification. The Source code is available at https://github.com/mahmad00/Conventional-to-Transformer-for-Hyperspectral-Image-Classification-Survey-2024.