Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-03, Vol.23 (7), p.3515
• Main Authors: Orozco, Jairo, Manian, Vidya, Alfaro, Estefania, Walia, Harkamal, Dhatt, Balpreet K
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; adaptive neighborhood; Agricultural production; Algorithms; Ambient temperature; Classification; Computer architecture; Deep learning; Eigenvectors; graph convolutional network; Graph neural networks; Graph representations; hyperspectral image classification; Hyperspectral imaging; hyperspectral rice seed images; Image classification; laplacian matrix; Machine learning; Methods; Neighborhoods; Neural networks; Pixels; Rice; Seeds; Spatial data; Spectral signatures
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23073515

Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network classification of hyperspectral images. The spatial-spectral information is integrated into the adjacency matrix and processed by a single-layer graph convolutional network. The algorithm employs an adaptive neighborhood selection criteria conditioned by the class it belongs to. Compared to fixed window-based feature extraction, this method proves effective in capturing the spectral and spatial features with variable pixel neighborhood sizes. The experimental results from the Indian Pines, Houston University, and Botswana Hyperion hyperspectral image datasets show that the proposed AN-GCN can significantly improve classification accuracy. For example, the overall accuracy for Houston University data increases from 81.71% (MiniGCN) to 97.88% (AN-GCN). Furthermore, the AN-GCN can classify hyperspectral images of rice seeds exposed to high day and night temperatures, proving its efficacy in discriminating the seeds under increased ambient temperature treatments.