Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification

TL;DR

This paper introduces a hybrid neural network combining 3-D CNN, 2-D CNN, and Bi-LSTM to improve hyperspectral image classification, achieving higher accuracy with fewer parameters than existing models.

Contribution

It proposes a novel neural network architecture that effectively learns spatial, spectral, and inter-layer features for hyperspectral image classification.

Findings

Achieved 99.83% accuracy on Indian Pines dataset.

Achieved 99.98% accuracy on University of Pavia dataset.

Achieved 100% accuracy on Salinas Scene dataset.

Abstract

Hyper spectral images have drawn the attention of the researchers for its complexity to classify. It has nonlinear relation between the materials and the spectral information provided by the HSI image. Deep learning methods have shown superiority in learning this nonlinearity in comparison to traditional machine learning methods. Use of 3-D CNN along with 2-D CNN have shown great success for learning spatial and spectral features. However, it uses comparatively large number of parameters. Moreover, it is not effective to learn inter layer information. Hence, this paper proposes a neural network combining 3-D CNN, 2-D CNN and Bi-LSTM. The performance of this model has been tested on Indian Pines(IP) University of Pavia(PU) and Salinas Scene(SA) data sets. The results are compared with the state of-the-art deep learning-based models. This model performed better in all three datasets. It could achieve 99.83, 99.98 and 100 percent accuracy using only 30 percent trainable parameters of the state-of-art model in IP, PU and SA datasets respectively.