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A new framework in the form of Polarimetric Synthetic Aperture Radar (PolSAR) image classification, where deep Convolutional Neural Networks (CNNs) were integrated with the traditional Machine Learning (ML) techniques under a Temporal Convolutional Network (TCN) architecture, was introduced in the paper. The main aim behind this new approach is to overcome the severe limitations inherent in both deep CNN and conventional ML approaches. The application of the sliding-window strategy eliminates the necessity of requiring extensive feature extraction procedures while reducing computational complexity simultaneously. Experiments on four benchmark PolSAR datasets for C-Band, L-Band, AIRSAR, and RADARSAT-2 data attest to the framework's remarkable classification accuracies in the range of 94.55% to 99.39%. This integrated framework is thus a significant advancement in PolSAR image analysis in offering an efficient methodology that combines the strengths of deep CNNs and traditional ML, by mitigating their respective limitations. It also combines the sliding-window technique with the architecture of TCN and then yields excellent classification accuracy with no much additional computational overhead. The results obtained thus indicate a good chance of revolutionizing the state of the art in PolSAR image classification, providing crucial efficiency improvements and making applications in environmental applications stronger, across almost all kinds of fields.

A new framework in the form of Polarimetric Synthetic Aperture Radar (PolSAR) image classification, where deep Convolutional Neural Networks (CNNs) were integrated with the traditional Machine Learning (ML) techniques under a Temporal Convolutional Network (TCN) architecture, was introduced in the paper. The main aim behind this new approach is to overcome the severe limitations inherent in both deep CNN and conventional ML approaches. The application of the sliding-window strategy eliminates the necessity of requiring extensive feature extraction procedures while reducing computational complexity simultaneously. Experiments on four benchmark PolSAR datasets for C-Band, L-Band, AIRSAR, and RADARSAT-2 data attest to the framework's remarkable classification accuracies in the range of 94.55% to 99.39%. This integrated framework is thus a significant advancement in PolSAR image analysis in offering an efficient methodology that combines the strengths of deep CNNs and traditional ML, by mitigating their respective limitations. It also combines the sliding-window technique with the architecture of TCN and then yields excellent classification accuracy with no much additional computational overhead. The results obtained thus indicate a good chance of revolutionizing the state of the art in PolSAR image classification, providing crucial efficiency improvements and making applications in environmental applications stronger, across almost all kinds of fields.

Smart agriculture holds a transformative potential, driven by cutting-edge technologies, in revolutionizing the global food production sector and enhancing food safety measures. This is achieved by leveraging the capabilities of smart maps, artificial intelligence, and agricultural data analytics. Hence, farmers can benefit from it as it allows them to streamline resource utilization. This, in turn, leads to reduced inputs of fertilizers, labor, seeds, and water, while simultaneously amplifying crops. Therefore, this paper introduces a comprehensive system for advancing smart map agriculture, comprising five pivotal stages designed to yield accurate and meaningful results. Notable progress is achieved through meticulous data acquisition by employing the rice seedling and WeedNet datasets, coupled with feature extraction utilizing the MobileNet architecture. The classification stage is critical for distinguishing between rice and land in the first dataset and between crops and weeds in the second one. It further enhances the system’s efficacy. Additionally, segmentation using K-means clustering to detect rice or crop is employed. Notably, the current approach exhibits promising performance levels. It results in 99.7%, 100%, 99.4%, and 99.72% for accuracy, precision, recall, and F1_score, respectively, in the rice seedling dataset, as well as achieving 98.72%, 97.3%, 100%, and 98.63% in the WeedNet dataset. The primary contribution of this study lies in the integration of MobileNet and SVM architectures for feature extraction and classification in the agriculture field. It also presents the innovative use of K-means clustering for segmenting agricultural fields.