A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

Abstract

AI-driven precision agriculture applications can benefit from the large data source that remote sensing (RS) provides, as it can gather agricultural monitoring data at various scales throughout the year. Numerous advantages for sustainable agricultural applications, including yield prediction, crop monitoring, and climate change adaptation, can be obtained from RS and artificial intelligence. In this work, we proposed a fully automated optimized self-attention fused convolutional neural network (CNN) architecture for land use and land cover classification using RS data. A new contrast enhancement equation has been proposed and utilized in the proposed architecture for the data augmentation. After that, a fused self-attention CNN architecture was proposed. The proposed architecture initially consists of two custom models named IBNR-65 and Densenet-64. Both models have been designed based on the inverted bottleneck residual mechanism and dense blocks. After that, both models were fused using a depth-wise concatenation and append a self-attention layer for deep features extraction. After that, we trained the model and performed classification using neural network (NN) classifiers. The results obtained from the NN classifiers are insufficient; therefore, we implemented a Bayesian optimization and fine-tuned the hyperparameters of NN. In addition, we proposed a quantum hippopotamus optimization algorithm for the best feature selection. The selected features are finally classified using fine-tuned NN classifiers and obtained improved accuracy of 98.20, 89.50, and 91.70%, and the highest precision rate is 98.23, recall is 98.20, and F1-score is 98.21, respectively, for SIRI-WHU, EuroSAT, and NWPU datasets. Moreover, a detailed ablation study was conducted, and the performance was compared with SOTA. The proposed architecture shows improved accuracy, sensitivity, precision, and computational time performance.

Keywords

• Agriculture
• data augmentation
• deep learning
• optimization
• remote sensing (RS)
• self-attention