Novel 3-D Deep Neural Network Architecture for Crop Classification Using Remote Sensing-Based Hyperspectral Images

Recent developments and the widespread adoption of remote sensing data (RSD) gave rise to various hyperspectral imaging (HSI) applications. With its detailed spectral information, HSI has been adopted for use in various agriculture-related applications. These applications demand more accurate classification, highlighting the significance of HSI in plant disease detection, crop classification, etc. Despite existing models for RSD-based agricultural applications, such models lack generalizability for plant classification. This task is challenging for the UNet-based architectures due to nonlinear combinations of the pixels in the hyperspectral image, reduced and diverse information of each pixel, and high dimensions. Furthermore, a shortage of labeled data makes achieving high classification accuracy challenging. This study proposes an improved 3-D UNet architecture based on a modified convolutional neural network that uses spatial and spectral information. This approach solves the limitations of existing UNet-based models, which suffer to deal with nonlinear combinations and reduced and diverse information of small pixels. The proposed model employs a semantic segmentation strategy with modified architecture for more accurate classification and segmentation. The studies employ widely recognized benchmark HSI datasets, such as the Indian Pines, Salinas, Pavia University, Honghu, and Xiong'an datasets. These datasets are assessed using average accuracy, overall accuracy, and the Kappa coefficient. The suggested model demonstrated exceptional classification accuracy, achieving 99.60% for the Indian Pines dataset and 99.67% for the Pavia University dataset. The proposed approach is further validated and proven to be superior and robust through comparisons with existing models.