Pixel-wise mechanical damage detection of waxy maize using spectral-spatial feature extraction and hyperspectral image

Mechanical damage detection on waxy maize is critical to guarantee maize quality and meet the product requirements. A novel spectral-spatial feature extraction enhanced fully connected neural network (SSFE-FCNN) classification method is proposed in this paper to achieve pixel-wise mechanical damage detection of waxy maize using the hyperspectral image (HSI). The proposed SSFE-FCNN utilizes the online tensor dictionary learning algorithm to extract spectral-spatial tensor features from HSI cubes. Moreover, the designed fully connected neural network extracts high-level features from the tensor features and provides classification results. The SSFE-FCNN combines the advantages of sparse tensor representation and neural networks to exploit joint spectral-spatial information in HSI and improve detection performance. Experimental results on the HSI of waxy maize show that the proposed SSFE-FCNN is superior to the support vector machine and the currently popular neural network methods. The overall accuracy of SSFE-FCNN in the pixel-wise classification of waxy maize achieves as high as 98.09%. The false and missing detection rates are 2.08% and 1.96%, respectively. Furthermore, pixel-wise detection can detect slight damage and perform the maize grading operation according to the damage ratio.