# Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/1902.10858 · HTML: https://mlsanity.com/paper/1902.10858/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 30 of 31)

- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model](https://mlsanity.com/paper/2406.11519/) (2024)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [SpectralMamba: Efficient Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.08489/) (2024)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing](https://mlsanity.com/paper/2105.10194/) (2021)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12003/) (2024)
- [DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2406.07050/) (2024)
- [HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification](https://mlsanity.com/paper/2404.00272/) (2024)
- [A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification](https://mlsanity.com/paper/2105.14327/) (2021)
- [SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification](https://mlsanity.com/paper/2104.00341/) (2021)
- [DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2304.09915/) (2023)
- [S2Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.18213/) (2024)
- [A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification](https://mlsanity.com/paper/2204.04462/) (2022)
- [Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery](https://mlsanity.com/paper/2308.15081/) (2023)
- [One-Class Risk Estimation for One-Class Hyperspectral Image Classification](https://mlsanity.com/paper/2210.15457/) (2022)
- [Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal](https://mlsanity.com/paper/2305.10640/) (2023)
- [HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding](https://mlsanity.com/paper/2311.14899/) (2023)
- [CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis](https://mlsanity.com/paper/2504.19223/) (2025)
- [Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2310.18549/) (2023)
- [A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset](https://mlsanity.com/paper/2407.16384/) (2024)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)
- [HyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder](https://mlsanity.com/paper/2505.05710/) (2025)
- [Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing](https://mlsanity.com/paper/2103.01449/) (2021)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2310.18550/) (2023)
- [Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly](https://mlsanity.com/paper/2503.16086/) (2025)
- [DisentangleFormer: Spatial-Channel Decoupling for Multi-Channel Vision](https://mlsanity.com/paper/2512.04314/) (2025)
- [State Space Models Meet Remote Sensing: A Survey](https://mlsanity.com/paper/2606.25329/) (2026)

## Abstract (arXiv)

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.