AUTHOR=Wang Bin , Chen Gongchao , Wen Juan , Li Linfang , Jin Songlin , Li Yan , Zhou Ling , Zhang Weidong 

TITLE=SSATNet: Spectral-spatial attention transformer for hyperspectral corn image classification

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 15 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1458978

DOI=10.3389/fpls.2024.1458978

ISSN=1664-462X

ABSTRACT=Hyperspectral images are rich in spectral and spatial information, providing a detailed and comprehensive description of objects, which makes hyperspectral image analysis technology essential in intelligent agriculture. With various corn seed varieties exhibiting significant internal structural differences, accurate classification is crucial for planting, monitoring, and consumption. However, due to the large volume and complex features of hyperspectral corn image data, existing methods often fall short in feature extraction and utilization, leading to low classification accuracy. To address these issues, this paper proposes a spectral-spatial attention transformer network (SSATNet) for hyperspectral corn image classification. Specifically, SSATNet utilizes 3D and 2D convolutions to effectively extract local spatial, spectral, and textural features from the data while incorporating spectral and spatial morphological structures to understand the internal structure of the data better. Additionally, a transformer encoder with cross-attention extracts and refines feature information from a global perspective. Finally, a classifier generates the prediction results. Compared to existing state-of-the-art classification methods, our model performs better on the hyperspectral corn image dataset, demonstrating its effectiveness.