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.

land use and land cover; synthetic aperture radar; gradient based optimizer; deep learning; residual network

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