A residual network with attention module for hyperspectral information of recognition to trace the origin of rice

Abstract

In this work, a neural network framework for hyperspectral information recognition was proposed, combined with residual block and convolutional block attention module (CBAM) to enhance the detection performance of hyperspectral for tracing the rice quality. Firstly, the hyperspectral image system was used to obtain the hyperspectral information of the rice. Secondly, due to the small data set, the structure of the residual network was designed based on the characteristics of the hyperspectral information to prevent overfitting the model. Finally, the CBAM was introduced to calculate the channel and spatial attention to redistribute the weight parameter and enhance the classification performance of the model. The results showed that our (Res-CBAM) model had better classification performance than other classification methods. The classification accuracy of the rice was 96.33%. This study provided a strategy to enhance the detection performance of hyperspectral, and an intelligent technology to trace the rice quality.

Keywords: Channel attention; Hyperspectral information; Residual block; Rice quality; Spatial attention.