Multimedia Decoding Analysis of Small Target Remote Sensing Network Based on Multimodal Deep Learning

Abstract

This study proposes an integrated approach for small target detection in remote sensing networks, leveraging multimedia decoding analysis and multimodal deep learning techniques. The methodology involves preprocessing remote sensing data, extracting relevant features, developing multimodal deep learning models, and evaluating performance metrics. Across various experiments, the developed models demonstrated remarkable accuracy rates ranging from 90% to 95%, with high precision and recall values exceeding 85% and 90%, respectively. Comparative analysis against state-of-the-art methods further validated the superior performance of the proposed methodology, highlighting its potential to advance small target detection capabilities within remote sensing networks. The findings have significant implications for domains such as environmental monitoring, disaster management, and national security, offering valuable insights and actionable information for decision-makers and stakeholders. Future research directions could focus on enhancing model robustness, scalability, and applicability to diverse environmental conditions, further advancing understanding and decision-making in remote sensing applications. The loss function serves as a crucial component that quantifies the difference between the predicted output of the model and the ground truth labels associated with the input data. In practice, the gradient of the loss function with respect to the parameters is computed using techniques such as backpropagation, which efficiently propagates the gradients backward through the computational graph of the model.