Remote detection and sensing of ground and environmental targets using artificial orifice radar (SAR) and deep learning
Subject Areas : New technologies in natural resources and environment
Gohar Varamini
1
*
,
Amin Eskandari
2
,
Amin Tousi
3
1 - Department of Electrical Engineering, Bey. C., Islamic Azad University, Beyza, Iran.
2 - دانشگاه آزاد اسلامی، واحد شیراز، گروه مهندسی برق و کامپیوتر، شیراز، ایران
3 - 1Department of Electrical Engineering, Shi. C., Islamic Azad University, Beyza, Iran.
Keywords: Artificial Window Radar (SAR), Deep Learning, Artificial Intelligence, Remote Sensing,
Abstract :
Introduction: The technology of detection, identification and detection of objects and the environment is one of the most important parameters in engineering and environmental sciences. It is also widely used in the field of deep learning and artificial intelligence domains and plays a significant role in recognizing, identifying, and reconstructing images. This technology is a prelude to many projects and algorithms in this field, in which objects are identified in satellite and radar images and separated from surplus information and data, and in the next stages, they are classified and finally measured.
Materials and Methods: The method of identifying ground targets by SAR radar is using deep learning and artificial neural networks. In the pre-processing stage, in order to reduce the destructive effects of noise and preserve the original data, the Lee filter has been used due to its better efficiency in improving image quality and better preservation of edges, and the separation of excess data from the original images has been used using the blind signal separation algorithm.
Results and Discussion: In this study, the environmental conditions of the geographical area have been investigated through remote sensing and the proposed method in order to classify the data from the combined deep learning network, the results of which have been compared with the methods proposed in the field of goal classification, and finally it is determined that the proposed network in terms of classification accuracy with 86.31% compared to other studied networks with accuracy and coefficient. Higher reliability can be checked and implemented. Finally, using YOLO and RCNN algorithms to investigate the results of the detection and identification of ground targets
It has been discussed that the RCNN algorithm has been denoised with higher accuracy in detecting and identifying images.
Conclusion: Finally, the performance and reliability of the proposed model can be implemented with more accuracy and very little error. It will create and provide a very desirable performance with a high reliability factor in reducing the destructive effects and better efficiency of the system
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