# Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

## TL;DR

This paper introduces a cascaded RNN model with GRUs for hyperspectral image classification, effectively capturing spectral redundancy and complementarity, and extends it with convolutional layers to incorporate spatial information, achieving superior results.

## Contribution

The paper proposes a novel cascaded RNN architecture with strategies to enhance spectral feature learning and extends it with convolutional layers for spectral-spatial analysis, improving classification performance.

## Key findings

- The cascaded RNN model outperforms existing methods on hyperspectral datasets.
- Incorporating convolutional layers enhances spectral-spatial classification accuracy.
- The proposed strategies effectively reduce spectral redundancy and exploit complementary information.

## 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.