Synthetic Aperture Radar Image Edge Research Articles

ViT-DexiNet: a vision transformer-based edge detection operator for small object detection in SAR images

ABSTRACTThis paper introduces a novel edge detection operator called ‘vision transformer-based Dexinet (ViT-DexiNet)’ to address the challenges of detecting small objects in synthetic aperture radar (SAR) images. SAR images are typically impacted by strong multiplicative noise, making edge detection difficult. Existing traditional methods have limited spectral data preservation capabilities and often result in a loss of clarity and integrity of salient features in SAR images. The proposed ViT-DexiNet operator employs a series of interconnected layers to extract and refine salient edge features from SAR images. It utilizes a vision transformer self-attention layer to capture the pattern and structural details of image features crucial for determining edges. The extracted feature maps are then processed by the DexiNet architecture, which consists of a dense block, transfer block, and upsampling network. This architecture helps preserve edge information at different scales in deeper layers. The series of layered blocks generate edge maps, which are concatenated and averaged through smoothing to remove noise and enhance edge details in SAR images, resulting in a final high-quality edge map. To evaluate the proposed ViT-DexiNet method, both qualitative and quantitative analyses are conducted using standard edge detection operators such as Canny and Sobel. The empirical results demonstrate that the ViT-DexiNet surpasses baseline edge detection operators. The achieved values of proposed edge detection operator are 97.92%, 97.72%, 97.64% and 97.41%, respectively for the metrics accuracy, precision, recall and f1-score. The ViT-DexiNet offers high-quality edge maps, simplifying the interpretation of data for small object detection. Overall, the ViT-DexiNet method shows promise in overcoming the limitations of traditional approaches and improving the detection of edges in SAR images.

Fully Size-Adaptive Sliding Window Method for SAR Image Edge Detection

The local statistics are useful for the synthetic aperture radar (SAR) image processing. However, unreliable statistical results are produced due to insufficient sample size. To overcome the statistical limitation, the appropriate size of sliding window is vital in the image processing. Therefore, a completely size-adaptive sliding window method is proposed in this letter. A method to determine the size boundaries of the sliding window based on the basic size information is firstly proposed. Then, a strategy for changing the sliding window size is used to generate the sliding window size index matrix of the image. Among the many image processing applications that use sliding windows, we choose the SAR image edge detection arbitrarily to verify the effectiveness of the proposed method. And the experimental results indicate that our method makes the original edge detection algorithms improved significantly. Further, the proposed size-adaptive sliding window method has potential for many other image processing tasks that require the use of local statistical information.

A Proposed Approach to Determine the Edges in SAR images

Radar is the most eminent device in the prolonged scattering era The mechanisms involve using electromagnetic waves to take Synthetic Aperture Radar (SAR) images for long reaching. The process of setting edges is one of the important processes used in many fields, including radar images, which assists in showing objects such as mobile vehicles, ships, aircraft, and meteorological and terrain forms. In order to accurately identify these objects, their edges must be detected. Many old-style methods are used to isolate the edges but they do not give good results in the determination process. Conservative methods use an operator to detect the edges, such as the Sobel operator which is used to perform edge detection where the edge does not appear well.
 The proposed method which combines Ridgelet transform, Bezier curve and Sobel operator is used to detect edges very efficiently. Ridghelet transform resolves the harms in the wavelet transform and it can well detect the edges in images. Bezier curve can profit gradual variation of the data and their mutability. Hence, the efficiency of the edged image is improved and, when used with Sobel operator, the quality of the edge image become very good. The data show that the advocated method has superior fallouts over the Sobel edge detection and the wavelet method in both subjective and impartial experiments. While the Peak Signal to Noise Ratio(PSNR) values were equal to 9.3812, 9.8918, 9.6521 and 9.0743using the Sobel operator method and to10.2564, 10.7927, 10.5612and 10.8633 using the wavelet method, they were increased in the proposed method to 12.6542, 12.9514, 12.8574 and 12.3013 respectively.

Ocean SAR Image Segmentation and Edge Gradient Feature Extraction

Ma, H.-L. and Zhang, L.-Y., 2019. Ocean SAR image segmentation and edge gradient feature extraction. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 141–144. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the segmentation effect of synthetic aperture radar (SAR) ocean oil spill image and obtain accurate information of oil spill on ocean surface. Current image segmentation techniques are based on two properties called discontinuity and similarity based. In the segmentation based on discontinuity, image segmentation is performed based on a change in the intensity of the gray level of the image. Isolated points, edges and lines are identified primarily by methods based on discontinuities. In the similarity-based segmentation, the same points, lines and edges are identified. It contains region segmentation and merging, thresholds, and region growth.

Change Detection of Water Index in Danjiangkou Reservoir Using Mixed Log-Normal Distribution Based Active Contour Model

The use of synthetic aperture radar (SAR) images for water segmentation can accurately extract the boundaries of water areas and is of great significance for studying the temporal and spatial changes of lakes and other environmental elements. In view of the fact that SAR image itself has characteristics such as speckle noise and large volumes, this paper proposes an edge active contour model (ACM) based on the mixed log-normal distribution for SAR image edge extraction and evaluates the parameters of distribution by using the classic expectation-maximization (EM) algorithm. Furthermore, compared with the existing models, the proposed algorithm introduces regional variable coefficients and modifies the evolution rate in the distance regularization term so that the level set can be quickly and accurately stopped at the target edge. For practical application, the proposed ACM is applied to extract the outline of the Danjiangkou Reservoir (DJKR) with time as a sequence. The experimental results show that it is robust to noise, improves the precision of land and water segmentation, and helps to determine the changing trends of indicators, such as water surface area, average water depth, and relative storage capacity.

SAR image edge detection via sparse representation

In this paper, we propose a new synthetic aperture radar (SAR) image detection algorithm based on the de-noising algorithm via the sparse representation and a new morphology edge detector. Firstly, we apply the Shearlet transform to the SAR image to get the sparse representation of it. Then, morphological edge detector with direction is applied to directional sub-band coefficients of the Shearlet which are recovered by the iterative de-noising process. Finally, the completed SAR image edge is obtained by merging each sub-band edge using Dempster–Shafer evidence theory. By completely using the directional sub-bands of the Shearlet transform, the proposed algorithm overcomes the disadvantages of transform detection algorithms which are very unrobust to noise and can also generate inaccurate edges. The experimental results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the edge positioning accuracy, integrity, and the number of false edge points.

Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random field modeling

Synthetic aperture radar (SAR) image is severely affected by multiplicative speckle noise, which greatly complicates the edge detection. In this paper, by incorporating the discontinuity-adaptive Markov random field (DAMRF) and maximum a posteriori (MAP) estimation criterion into edge detection, a Bayesian edge detector for SAR imagery is accordingly developed. In the proposed detector, the DAMRF is used as the a priori distribution of the local mean reflectivity, and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient–descent method. Four normalized ratios constructed in different directions are computed, based on which two edge strength maps (ESMs) are formed. The final edge detection result is achieved by fusing the results of two thresholded ESMs. The experimental results with synthetic and real SAR images show that the proposed detector could efficiently detect edges in SAR images, and achieve better performance than two popular detectors in terms of Pratt’s figure of merit and visual evaluation in most cases.

Bionic vision-based synthetic aperture radar image edge detection method in non-subsampled contourlet transform domain

For synthetic aperture radar (SAR) images, traditional edge detection methods can hardly extract the complete and true edges since they are sensitive to noise. In this study, the authors propose an edge detection method based on bionic vision in non-subsampled contourlet transform (NSCT) domain for SAR images, which makes use of the characteristics of NSCT (e.g. multiscale, multidirection edge expression and outstanding edge location) and the mechanisms of strengthening and positioning the image edge by fixational eye movements. First, source images are decomposed in the NSCT domain. The minimum mean squared error (MMSE) filter in the NSCT domain is adopted to reduce speckle noise of SAR images. Movement, difference and competition are applied on each subband to obtain original multiscale and multidirection subband edges. Lipschitz regularity and non-maximum suppression are also adopted to effectively remove noise points. Finally, hysteresis thresholding with adaptive thresholds is introduced to avoid streaking and trial-and-error, and multiscale edge fusion is used to reduce the false alarm rate. Edge extraction results of simulated and real SAR images demonstrate that the proposed method is better than other edge detection methods based on Canny operator, ratio of averages (ROA) operator, ratio of exponentially weighted averages (ROEWA) operator, wavelet modulus maximum and curvelet.