A Novel Polarimetric SAR Classification Method Integrating Pixel-Based and Patch-Based Classification

Yang, Ray Yeng; Hu, Zhentao; Liu, Yiming; Xu, Zhixiang

doi:10.1109/lgrs.2019.2923403

Cited by 19 publications

(13 citation statements)

References 20 publications

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“…According to the experiment results in this letter, as long as the accuracy of the classification result exceeds certain threshold, good registration performance can be guaranteed. Through the investigation of existing remote sensing image classification algorithms, the accuracy of current feature classification already exceeded this required threshold [5–13]. This proves the feasibility of the regional‐feature‐based registration scheme.…”

Section: Discussionmentioning

confidence: 96%

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Analysis of feasibility and advantages of multi‐source image registration using regional features

Zeng

Zhu

et al. 2021

Electronics Letters

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Automatic registration of multi-source remote sensing data is a challenging task due to the high non-linearity of radiometric differences among various data. Feature extraction is a key enabling technique in registration algorithm design. In this letter, we prove the feasibility of using extracted regional features for multi-source image registration. In particular, regional features can be extracted through pixel-level image classification, and existing off-the-shelf algorithms can be directly adopted. Experiment results demonstrate the robustness of the regionalfeature-based registration method.

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Section: Discussionmentioning

confidence: 96%

“…[6] achieve an overall accuracy of 95.17%. Other recently proposed method typically reaches an accuracy of above 90% [7–13].…”

Section: Introductionmentioning

confidence: 99%

Analysis of feasibility and advantages of multi‐source image registration using regional features

Zeng

Zhu

et al. 2021

Electronics Letters

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“…In this way, improved and smoother classification results can be achieved. However, some methods utilize image patches or superpixels as classification units, which may cause classification errors in certain areas and may not better preserve the contours of certain ground targets [40]. Additionally, the patch-based methods will increase computational complexity and load.…”

Section: Introductionmentioning

confidence: 99%

PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net

Cao

et al. 2021

Remote Sensing

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The presence of speckles and the absence of discriminative features make it difficult for the pixel-level polarimetric synthetic aperture radar (PolSAR) image classification to achieve more accurate and coherent interpretation results, especially in the case of limited available training samples. To this end, this paper presents a composite kernel-based elastic net classifier (CK-ENC) for better PolSAR image classification. First, based on superpixel segmentation of different scales, three types of features are extracted to consider more discriminative information, thereby effectively suppressing the interference of speckles and achieving better target contour preservation. Then, a composite kernel (CK) is constructed to map these features and effectively implement feature fusion under the kernel framework. The CK exploits the correlation and diversity between different features to improve the representation and discrimination capabilities of features. Finally, an ENC integrated with CK (CK-ENC) is proposed to achieve better PolSAR image classification performance with limited training samples. Experimental results on airborne and spaceborne PolSAR datasets demonstrate that the proposed CK-ENC can achieve better visual coherence and yield higher classification accuracies than other state-of-art methods, especially in the case of limited training samples.

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“…As a branch of machine learning, deep learning has made significant progress in recent years and gradually developed into a research hotspot, compared with the machine vision methods, convolution neural networks (Xie, Ma, & Zhao, 2020; Qiao, Li, & Su, 2020; Ji, Zhang, & Zhang, 2020; Hazar, Sahar, & Emna, 2020; Dong, Pan, & Cen, 2020; Xiao, Wu, & Hu, 2020; Yang, HU, & Liu, 2020; Li et al, 2020b; Li & Yang, 2020; Li & Chao, 2020) have more powerful feature learning and representation capabilities, and can automatically learn features, so as to achieve better classification results. Therefore, it has a broad application prospect in quality grading of jujubes.…”

Section: Introductionmentioning

confidence: 99%

Quality grading of jujubes using composite convolutional neural networks in combination with RGB color space segmentation and deep convolutional generative adversarial networks

Guo

Zheng

et al. 2020

J Food Process Engineering

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As an important link in the processing of jujube products, the qualities classification of jujubes have an important impact on improving the value of commodities. In this study, jujube target was extracted based on the RGB color space characteristics and then put into a black background through a mask. The data augmentation method combined deep convolutional generative adversarial networks and rigid transformation (RT) was used to improve the data richness of defective jujubes, effectively solve the imbalance problem between different types of jujube data. A composite convolutional neural network (CNN) method based on residual networks was designed to effectively solve the problem of misjudgment between jujubes with subtle defects and healthy jujubes. The overall results illustrated that the defect detection accuracy of the proposed scheme was 99.2%, which was superior to the widely used support vector machine and CNN methods. This work could be applied to the actual processing site and greatly improved the quality classification effect of jujubes.Practical ApplicationsCracks, peeling, wrinkles, and other defects have seriously affected the quality and value of jujubes, and the quality classification of jujubes is imperative. This paper proposes a set of deep learning schemes from three aspects of improving data quality, enhancing data richness, and designing more accurate and effective classification models. Experimental results show that this scheme can significantly improve the accuracy of jujube quality grading.

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A Novel Polarimetric SAR Classification Method Integrating Pixel-Based and Patch-Based Classification

Cited by 19 publications

References 20 publications

Analysis of feasibility and advantages of multi‐source image registration using regional features

Analysis of feasibility and advantages of multi‐source image registration using regional features

PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net

Quality grading of jujubes using composite convolutional neural networks in combination with RGB color space segmentation and deep convolutional generative adversarial networks

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