Abstract
How to improve utility performance when securing sensitive data is an important research problem in Internet of smart sensors. In this paper, we study secured image speckle denoising for networked synthetic aperture radar (SAR). Speckle noise of SAR affects image quality and has a great influence on target detection and recognition. MSTAR dataset is often used in image target recognition. In this paper, a subregion-based method is proposed in order to improve the accuracy of target recognition and better retain target information while filtering and denoising the image. The new method applies advanced encryption techniques to protect sensitive data against malicious attack. Firstly, the image is divided into marked areas and unmarked areas through edge extraction and hole filling. Secondly, we use different size windows and filtering methods to filter the image in different areas. The experimental results show that the proposed algorithm has obvious advantages over MR-NLM, SSIM-NLM, Frost, and BM3D filtering in terms of equivalent view number and preserving edge and structure.
Highlights
Synthetic aperture radar (SAR) is a system of continuous tracking and monitoring imaging that can transmit and receive electromagnetic waves
Experimental data were obtained from the measured SAR ground stationary target data. ey were published by the MSTAR project supported by DARPA
A fixed area is selected in the image and marked by a white box in the original image, and the equivalent number of each algorithm is calculated in this fixed area. en, the search window radius of 5 and similar block radius of 2 and the search window radius of 10 and similar block radius of 3 were defined for the unmarked area, respectively, in order to analyze the effectiveness of the new weighting function in smoothing noise
Summary
Synthetic aperture radar (SAR) is a system of continuous tracking and monitoring imaging that can transmit and receive electromagnetic waves. When the radar networking is designed, the security of received data and the stability of the system need to be considered. Cyber security is very important in different netting systems. When the radar networking system is utilized, the data from single radar and different processing flows should be encrypted and kept secure. Malware and software vulnerability detections based on machine learning and deep neural models are studied to ensure that the Internet system is secure [6,7,8]. Erefore, speckle suppression of SAR image is extremely important for feature extraction of image and target recognition Due to the limitation of the imaging mechanism of radar, the complexity of ground environment usually makes the reflection of the surrounding environment to electromagnetic waves weaken the reflection of the target to electromagnetic waves. erefore, speckle suppression of SAR image is extremely important for feature extraction of image and target recognition
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