SSTNet: Spatial, Spectral, and Texture Aware Attention Network Using Hyperspectral Image for Corn Variety Identification
Weidong Zhang, Zexu Li, Hai-Han Sun, Qiang Zhang, Peixian Zhuang, Chongyi Li
Abstract
Currently, most existing methods using hyperspectral image to assist seed identification only consider the spectral information but ignore the spatial information resulting in unsatisfactory classification results. To cope with this issue, we propose a spatial, spectral, and texture-aware attention network to identify corn varieties, called SSTNet. Specifically, we first employ 3D convolution to extract the spatial and inter-spectral features. Subsequently, we utilize 2D convolution to extract the spatial and texture features. Meanwhile, we embed an attention mechanism into the 2D convolution module to further refine the spatial and texture features. The advantageous complementary properties of 3D and 2D convolutions allow the spatial and textural features of hyperspectral images to be fully exploited. Besides, we construct a hyperspectral image dataset including 1200 samples of 10 corn varieties. Experiments on our proposed dataset demonstrate that our SSTNet outperforms the state-of-the-art methods for identifying corn varieties.