Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 13; p. 2328
• Main Authors: Wu, Ke, Zhan, Yanting, An, Ying, Li, Suyi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: 3D asymmetric decomposition convolution; Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Balances (scales); Brain; Classification; Comparative analysis; Convolution; Deep learning; Feature maps; graph convolutional networks; Graphs; hyperspectral image classification; Hyperspectral imaging; Image classification; Image processing; Machine learning; Methods; Multilayers; Multispectral photography; neural architecture search; Neural networks; Regularization methods; Remote sensing; Searching; Segmentation; Structured data; Technology application; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16132328

With the development of hyperspectral sensors, the availability of hyperspectral images (HSIs) has increased significantly, prompting advancements in deep learning-based hyperspectral image classification (HSIC) methods. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains, and have been used for HSIC. The superpixel segmentation should be implemented first in the GCN-based methods, however, it is difficult to manually select the optimal superpixel segmentation sizes to obtain the useful information for classification. To solve this problem, we constructed a HSIC model based on a multiscale feature search-based graph convolutional network (MFSGCN) in this study. Firstly, pixel-level features of HSIs are extracted sequentially using 3D asymmetric decomposition convolution and 2D convolution. Then, superpixel-level features at different scales are extracted using multilayer GCNs. Finally, the neural architecture search (NAS) method is used to automatically assign different weights to different scales of superpixel features. Thus, a more discriminative feature map is obtained for classification. Compared with other GCN-based networks, the MFSGCN network can automatically capture features and obtain higher classification accuracy. The proposed MFSGCN model was implemented on three commonly used HSI datasets and compared to some state-of-the-art methods. The results confirm that MFSGCN effectively improves accuracy.