Gradient based optimizer with deep learning based agricultural land use and land cover classification on SAR data

Abstract

Agricultural land use and land cover (LULC) classification using synthetic aperture radar (SAR) data is a fundamental application in remote sensing and precision agriculture. Leveraging the abilities of SAR, which can enter over cloud cover and deliver detailed data about surface features, allows a robust analysis of agricultural landscapes. By harnessing the control of SAR data and innovative deep learning (DL) methods, this technique provides a complete solution for effectual and automatic agricultural land classification, paving the method for informed decision-making in present farming systems. This study introduces a new gradient based optimizer with deep learning based agricultural land use and land cover classification (GBODL-ALULC) technique on SAR data. The GBODL-ALULC technique aims to detect and classify distinct types of land cover that exist in the SAR data. In the GBODL-ALULC technique, the feature extraction process takes place by a residual network with a convolutional block attention mechanism (ResNet-CBAM) model. At the same time, the GBO system has been executed for the best hyperparameter choice of the ResNet-CBAM model which helps to improve the overall LULC classification results. Finally, a regularized extreme learning machine (RELM) algorithm has been for the detection and classification of land covers. The performance study of the GBODL-ALULC method is carried out on the SAR dataset. The simulation outcome depicted that the GBODL-ALULC methodology reaches effectual LULC classification outcomes over compared methods.