Interval Group Spatial-Spectral Mamba: A Lightweight Framework for Enhanced Hyperspectral Image Classification using Selective State Space Models

Keskeiset käsitteet

This paper introduces IGroupSS-Mamba, a novel deep learning framework for hyperspectral image classification that leverages the strengths of Selective State Space Models (SSMs) in a computationally efficient manner to achieve state-of-the-art classification accuracy.

Tiivistelmä

Bibliographic Information:

He, Y., Tu, B., Jiang, P., Liu, B., Li, J., & Plaza, A. (2024). IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, X(X).

Research Objective:

This paper aims to address the limitations of existing deep learning models for hyperspectral image (HSI) classification, particularly in handling the high dimensionality and information redundancy of HSI data, by proposing a lightweight yet powerful framework called IGroupSS-Mamba.

Methodology:

The proposed IGroupSS-Mamba framework employs a hierarchical structure with multiple stages, each incorporating a downsampling operation and an Interval Group Spatial-Spectral Block (IGSSB). The IGSSB leverages an Interval Group S6 Mechanism (IGSM) to perform interval-wise feature grouping and parallel unidirectional sequence scanning along both spatial and spectral dimensions using Selective State Space Models (SSMs). This approach enables efficient global spatial-spectral feature extraction while mitigating information redundancy.

Key Findings:

• IGroupSS-Mamba significantly outperforms state-of-the-art HSI classification methods in terms of overall accuracy, average accuracy, and Kappa coefficient on three benchmark datasets: Indian Pines, Pavia University, and Houston 2013.
• The proposed interval grouping strategy effectively reduces computational costs while leveraging the complementary strengths of different scanning directions.
• The hierarchical structure with downsampling operations facilitates multi-scale spatial-spectral semantic learning, further enhancing classification accuracy.

Main Conclusions:

IGroupSS-Mamba presents a novel and effective solution for HSI classification by combining the advantages of SSMs, interval grouping, and hierarchical feature learning. The proposed framework achieves state-of-the-art performance with reduced computational complexity compared to existing methods.

Significance:

This research contributes to the advancement of HSI classification by introducing a computationally efficient and highly accurate framework that addresses the challenges posed by the high dimensionality and information redundancy of HSI data. The proposed IGroupSS-Mamba has the potential to improve the performance of various remote sensing applications.

Limitations and Future Research:

Future work could explore the integration of attention mechanisms within the IGSM to further enhance the model's ability to selectively focus on relevant spatial-spectral features. Additionally, investigating the application of IGroupSS-Mamba to other remote sensing tasks, such as object detection and change detection, could be promising research directions.

Tilastot

The Pavia University dataset encompasses 103 spectral bands and 610 × 340 pixels, with a spatial resolution of 1.3 m per pixel.
The Indian Pines dataset consists of 200 spectral bands and 145 × 145 pixels, with a spatial resolution of 20 m per pixel.
The Houston 2013 dataset comprises 144 spectral bands and 340 × 1905 pixels, with a spatial resolution of 2.5 m per pixel.
The experiments on the Indian Pines, Pavia University, and Houston 2013 datasets were conducted with 10%, 5%, and 10% of the labeled samples, respectively.
The PCA dimension for reduction was set to 30.
The state dimension and expansion ratio in the S6 mechanism were fixed at 16 and 1, respectively.
The optimal patch size for IGroupSS-Mamba was determined to be 13 × 13.
The embedding dimension was set to 32, and the stage depth was determined as 3.
The downsample scale [2, 1] was uniformly applied across all three datasets.

Lainaukset

"Recent Mamba [19] built upon the State Space Models (SSMs) establish long-distance dependency through state transitions, which enjoys the advantages of global contextual modeling, linear computational complexity, and selective information processing."
"The high dimensionality of HSIs inevitably imposes substantial computational burdens."
"Adjacent spectral bands in original HSIs typically exhibit high similarity."
"Traditional multi-directional scanning strategy applied to all spectral bands may result in information redundancy."
"Given the abundant spectral information and strong spatial correlation inherent in HSIs, sequence scanning along only the spectral or spatial dimension may lead to the loss of spatial or spectral information, respectively."

Tärkeimmät oivallukset

IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification

by Yan He, Bing... klo arxiv.org 10-08-2024

https://arxiv.org/pdf/2410.05100.pdf

Syvällisempiä Kysymyksiä

How does the computational efficiency of IGroupSS-Mamba compare to Transformer-based methods in practical HSI classification scenarios with large datasets?

IGroupSS-Mamba demonstrates superior computational efficiency compared to Transformer-based methods, especially when applied to large HSI datasets. This efficiency stems from the following key aspects:

Linear Complexity of S6: Unlike Transformers that rely on self-attention mechanisms with quadratic complexity (O(N²)), IGroupSS-Mamba leverages the Selective State Space Model (S6), which boasts linear complexity (O(N)) due to its efficient recursive computation. This difference in scaling becomes increasingly significant as the input sequence length (number of pixels in HSI) grows.
Interval Grouping Strategy: By partitioning the high-dimensional hyperspectral data into smaller, non-overlapping groups, IGroupSS-Mamba reduces the computational burden on the S6 modules. This grouping allows for parallel processing and reduces the overall number of computations required.
Lightweight Design: IGroupSS-Mamba is designed with a focus on lightweight architecture. The use of depth-wise convolutions and a limited number of channels in the IGSM further contributes to its computational efficiency.
In practical HSI classification scenarios with large datasets, these factors culminate in faster training times, reduced memory footprint, and potentially lower inference times for IGroupSS-Mamba compared to Transformer-based counterparts. This efficiency makes IGroupSS-Mamba a more practical choice for real-world applications where computational resources might be limited.

Could the reliance on a fixed interval grouping strategy limit the model's ability to adapt to varying degrees of spectral similarity between adjacent bands in different HSI datasets?

Yes, the fixed interval grouping strategy in IGroupSS-Mamba could potentially limit its adaptability to HSI datasets with varying spectral characteristics. Here's why:

Dataset-Specific Spectral Correlation: Different HSI datasets, acquired by different sensors or under different conditions, often exhibit varying degrees of spectral similarity between adjacent bands. A fixed interval might not be optimal for all cases.
Loss of Information: In cases where highly correlated bands are grouped separately due to the fixed interval, the model might miss out on capturing crucial local spectral-spatial patterns.
To address this limitation, several potential improvements could be considered:

Adaptive Grouping: Instead of a fixed interval, exploring adaptive grouping strategies based on the spectral correlation matrix of the specific HSI dataset could enhance adaptability. This could involve clustering algorithms or learning-based approaches to determine optimal group assignments.
Overlapping Groups: Introducing overlapping groups, where adjacent bands can belong to multiple groups, could mitigate information loss and allow the model to capture correlations at various spectral resolutions.
Learnable Grouping: Incorporating learnable parameters to dynamically adjust the grouping strategy during training could enable the model to automatically discover the most informative grouping for a given dataset.
By incorporating such adaptive mechanisms, IGroupSS-Mamba could become more robust and generalize better across diverse HSI datasets with varying spectral properties.

Considering the increasing availability of hyperspectral and LiDAR data fusion, how could the IGroupSS-Mamba framework be extended to effectively leverage the complementary information from both modalities for enhanced classification performance?

The IGroupSS-Mamba framework can be extended to effectively fuse hyperspectral and LiDAR data by incorporating mechanisms that leverage the complementary strengths of both modalities. Here are a few potential approaches:

Multimodal Input Fusion:

Early Fusion: Modify the initial pixel embedding layer of IGroupSS-Mamba to accept both hyperspectral and LiDAR data as input. This could involve concatenating features extracted from both modalities or using 3D convolutional layers to process the combined data.
Late Fusion: Process hyperspectral and LiDAR data through separate branches of IGroupSS-Mamba, each with its own set of IGSSB blocks. The features from both branches can then be fused at a later stage, such as before the final classification layer, using concatenation or attention mechanisms.

Cross-Modal Attention: Introduce cross-modal attention mechanisms within the IGSSB blocks to allow for information exchange between the hyperspectral and LiDAR feature representations. This would enable the model to selectively focus on relevant spatial details from LiDAR data while leveraging the rich spectral information from hyperspectral data.

Hybrid Spatial-Spectral-Elevation Blocks: Extend the IGSSB blocks to incorporate elevation information from LiDAR data. This could involve adding an elevation dimension to the input features and modifying the IGSM to perform sequence scanning along this dimension as well.

Joint Loss Function: Employ a joint loss function that considers both hyperspectral and LiDAR data during training. This would encourage the model to learn a representation that is discriminative for classification based on the combined information from both modalities.
By incorporating these extensions, the IGroupSS-Mamba framework can effectively leverage the complementary information from hyperspectral and LiDAR data, leading to improved classification accuracy, particularly in distinguishing objects with similar spectral signatures but different structural properties.