A Novel Hybrid Edge Detection Method for Polarimetric SAR Images

Shi, Jianyou; Jin, Haiyan; Xiao, Zhaolin

doi:10.1109/access.2020.2963989

Cited by 14 publications

(7 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on these two detectors, a wavelet-based hybrid edge detection method is proposed combining its advantages and disadvantages. The experimental results show that the proposed method can overcome the problems experienced [37].…”

Section: Introductionmentioning

confidence: 89%

Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

Andini¹,

Wanto²,

Devita³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

Edge detection is fundamental to Figure processing. Edges include much information in a figure, including the object's location, shape, size, and information about its texture. Since edge detection is a critical component of Figure processing for object detection, comprehend algorithms for edge detection. This is because the edges define an item's contours, serve as a demarcation between the object and its backdrop, and serve as a demarcation between overlapping objects. That is, if the edges of an image can be identified accurately, all things can be found. The proposal of this paper is the use of the Canny Zerocross hybrid method to perform better edge detection based on comparative studies and the incorporation of the Canny way, which is considered one of the best edge detection methods, with the Zerocross way (cross zero) which is a derivative of the laplacian. In this paper, the research data used is the retinal image dataset-data obtained from STARE (Structured Analysis of the Retina). The Veterans Administration Medical Center in San Diego and the Shiley Eye Center (ECS) at the University of California provided Figures and clinical data from the retinal images. The experimental results of the comparative study show that the Zerocross edge detection technique is better than the Canny edge detection technique. Meanwhile, edge detection and image identification would be better when combining the two methods (hybrid) based on merging studies.

show abstract

Section: Introductionmentioning

confidence: 89%

Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

Andini¹,

Wanto²,

Devita³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

show abstract

“…In order to achieve this objective, the proposed method combines concepts of two polarimetric and weighted gradients. The polarimetric method helps to find a weak and strong edge, while the weighted gradient method process an image which has heterogeneous features [38]. Chengtao C et al presented a paper for the implementation of a trilateral filter for image edge enhancement.…”

Section: Wavelet Based Methodsmentioning

confidence: 99%

An Astute Automaton Model for Objects Extraction Using Outer Totality Cellular Automata (OTCA)

Sharma,

Basheer

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Object formation is imperative to the recent computer vision, pattern recognition, healthcare, and automation applications. The objects are generated from images by defining edges and the segmentation process. This article introduced a novel method, Outer Totality Cellular Automata (OTCA), for defining actual and continuous edges of the image objects. The OTCA analysis nearby 25 neighbourhood pixels of all the pixels and generate a unique and efficient threshold. The proposed method has three primary functions, i.e. vitality, rule mapping, and improved morphological functions. The key objectives are image smoothing, neighbourhood analysis, defining game of life rule, and edges smoothing. Notably, the proposed method aimed to segment different coloured images, i.e., RGB, HSV, and YUV. The proposed method also aimed to produce more truthful results on blurred, reflected, shaded night vision images. The experimental process demonstrates using standard open-source datasets and validated using image quality assessment parameters, i.e., entropy, PSNR, SSIM, and MSE. The results claim 3% − 12% more structural analogous, factual, and accurate than existing classical methods and recent searches.

show abstract

“…Since the diagonal terms of the coherency matrix have been proved to be multiplicative noise model [40], the ratio operator can suppress the speckle noises of the three channels' and SPAN data. Hence, the edge and line energies [42] are defined as:…”

Section: A: Texture Features (4 Features)mentioning

confidence: 99%

A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification

Shi

Jin

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important study areas for PolSAR image processing. Many kinds of PolSAR features can be extracted for PolSAR image classification, such as the scattering, polarimetric or image features. However, it is difficult to improve the classification accuracy of PolSAR images by using all these low-level features directly, since they may conflict with each other for classification. Hence, how to joint learn these lowlevel features to obtain high-level discriminating features is a challenging task. To solve this problem, a novel fast multi-feature joint learning method(fMF-JLC) is proposed for PolSAR image classification. The proposed method extract three kinds of low-level features of PolSAR data at first. Then, a multifeature joint sparse representation model(MF-JSR) is proposed by designing joint sparse constraints on the extracted features above. Moreover, the joint sparse features are further compressed to overcome the dimension curse and acquire semantic features by the topic model. By this way, the low-level features are fused and discriminating high-level features are acquired. However, the pixel-wise feature learning method is time consuming. To speed the proposed method, a superpixel-based fast learning method is designed by involving the contextual relationship. Experiments are taken on three sets of real PolSAR data with different sensors and bands, and several compared methods are used to verify the effectiveness of the proposed method. The experimental results illustrate that the proposed method can obtain better performance than the state-ofart methods, especially for the heterogenous areas. INDEX TERMS Polarimetric SAR classification, joint multi-feature sparse representation, joint multifeature learning, fast classification method.

show abstract

A Novel Hybrid Edge Detection Method for Polarimetric SAR Images

Cited by 14 publications

References 43 publications

Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

An Astute Automaton Model for Objects Extraction Using Outer Totality Cellular Automata (OTCA)

A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification

Contact Info

Product

Resources

About