Cascaded Recurrent Neural Networks for Hyperspectral Image Classification (1902.10858v1)

Abstract: By considering the spectral signature as a sequence, recurrent neural networks (RNNs) have been successfully used to learn discriminative features from hyperspectral images (HSIs) recently. However, most of these models only input the whole spectral bands into RNNs directly, which may not fully explore the specific properties of HSIs. In this paper, we propose a cascaded RNN model using gated recurrent units (GRUs) to explore the redundant and complementary information of HSIs. It mainly consists of two RNN layers. The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from non-adjacent spectral bands. To improve the discriminative ability of the learned features, we design two strategies for the proposed model. Besides, considering the rich spatial information contained in HSIs, we further extend the proposed model to its spectral-spatial counterpart by incorporating some convolutional layers. To test the effectiveness of our proposed models, we conduct experiments on two widely used HSIs. The experimental results show that our proposed models can achieve better results than the compared models.

• The paper introduces a cascaded RNN architecture that employs dual GRU layers to reduce spectral redundancy and integrate complementary information from non-adjacent bands.
• The model extends to incorporate spatial features via convolution layers, significantly improving overall accuracy, average accuracy, and kappa metrics.
• The study presents a systematic framework for addressing hyperspectral data challenges and sets the stage for future work in hyperparameter optimization and hybrid deep learning architectures.

Overview of "Cascaded Recurrent Neural Networks for Hyperspectral Image Classification"

The paper presents a cascaded recurrent neural network (RNN) model employing gated recurrent units (GRUs) for the task of hyperspectral image (HSI) classification. Hyperspectral imaging generates vast amounts of spectral data, and effective classification is a crucial challenge due to the high dimensionality and the presence of redundant and complementary information among spectral bands.

Methodology

The proposed approach involves a novel cascaded architecture consisting of two RNN layers. The first RNN layer targets the reduction of redundancy in adjacent spectral bands, while the second aims to leverage the complementary information from non-adjacent bands. This dual-layer structure is designed to enhance the learning and discrimination capabilities of the network.

Key Features:

• Spectral Grouping: The spectral data is divided into sub-sequences, each processed by the first RNN layer to address redundancy.
• Complementary Learning: The second RNN layer processes the aggregated outputs of the first to capture complementary spectral information.
• Adaptive Strategies: Two strategies are introduced—feature-level and output-level improvements—to refine the feature learning process by integrating interactions between the RNN layers and the output layer.

Furthermore, the model is extended to incorporate spatial features through convolutional layers, forming a spectral-spatial model. This addition is aimed at exploiting the rich spatial information inherent in HSIs alongside spectral data, improving classification accuracy significantly.

Results

The experiments conducted on two benchmark datasets demonstrate the superiority of the proposed models over several existing methods, including SVM, 1D-CNN, and 2D-CNN. Notably, the spectral-spatial version of the cascaded RNN (SSCasRNN) achieved remarkable improvements in overall accuracy (OA), average accuracy (AA), and Kappa metrics, illustrating its effectiveness in integrating spectral and spatial features for enhanced classification performance.

Implications and Future Directions

This paper contributes a systematic methodology for addressing the curse of dimensionality inherent in HSI data by strategically combining recurrent neural networks with specific modifications tailored to spectral characteristics. The inclusion of spatial information through convolutional methods extends the utility of the model.

Future Work:

The paradigm established in this paper opens several avenues for exploration:

• Parameter Optimization: Further investigation into the optimization of hyperparameters, particularly the size of RNN layers and the number of sub-sequences, could refine the model's efficiency and accuracy.
• Exploration of Other Architectures: Exploring alternative deep learning architectures and hybrid models may yield further improvements in processing HSIs.
• Application to Other Domains: Adapting the proposed techniques for other types of spectral data or related fields might provide additional insights into their versatility and broader applicability.

Conclusion

The cascaded RNN model for HSI classification provides a robust framework that skillfully integrates spectral redundancy reduction and complementary information learning. The promising results depicted in the experiments suggest that this approach sets a strong foundation for developing advanced models capable of tackling the intricate challenges posed by hyperspectral data processing. The proposed methodologies and insights could potentially be extended, refined, and applied to a broad spectrum of real-world remote sensing applications.