Convolutional Kernel‐based covariance descriptor for classification of polarimetric synthetic aperture radar images

Imani, Maryam

doi:10.1049/rsn2.12204

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…The covariance matrix space is a Riemannian manifold, prompting the proposal of a covariance descriptor using affine, invariant Riemannian measures [25]. Maryam [26] constructed a covariance descriptor based on the feature map extracted by convolution, and utilized a support vector machine with a logarithm of a matrix kernel for image classification using the polarimetric synthetic aperture radar (PolSAR), yielding promising results. Li et al [27] proposed the local log-Euclidean multivariate Gaussian descriptor (L 2 EMG), demonstrating its effectiveness in image classification.…”

Section: Related Workmentioning

confidence: 99%

Lie Group Equivariant Convolutional Neural Network Based on Laplace Distribution

Liao,

Liu

2023

Remote Sensing

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Traditional convolutional neural networks (CNNs) lack equivariance for transformations such as rotation and scaling. Consequently, they typically exhibit weak robustness when an input image undergoes generic transformations. Moreover, the complex model structure complicates the interpretation of learned low- and mid-level features. To address these issues, we introduce a Lie group equivariant convolutional neural network predicated on the Laplace distribution. This model’s Lie group characteristics blend multiple mid- and low-level features in image representation, unveiling the Lie group geometry and spatial structure of the Laplace distribution function space. It efficiently computes and resists noise while capturing pertinent information between image regions and features. Additionally, it refines and formulates an equivariant convolutional network appropriate for the Lie group feature map, maximizing the utilization of the equivariant feature at each level and boosting data efficiency. Experimental validation of our methodology using three remote sensing datasets confirms its feasibility and superiority. By ensuring a high accuracy rate, it enhances data utility and interpretability, proving to be an innovative and effective approach.

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Section: Related Workmentioning

confidence: 99%

Lie Group Equivariant Convolutional Neural Network Based on Laplace Distribution

Liao,

Liu

2023

Remote Sensing

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“…These issues harden the classification process. So, extraction of appropriate features is a necessary task to have an efficient classification [3].…”

Section: Introductionmentioning

confidence: 99%

Spatial and polarimetric information fusion using residual network for polarimetric synthetic aperture radar image classification

Imani

2022

IET Radar Sonar & Navi

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A polarimetric synthetic aperture radar (POLSAR) system provides an image that can be considered as a data cube containing spatial information in two spatial dimensions and polarimetric information in the scattering dimension. A spatial and polarimetric residual network (SPRN) is proposed for POLSAR image classification. At first, polarimetric features are extracted from the scattering dimension through two designed polarimetric residual blocks. Then, the processed POLSAR cube is fed to two consecutive spatial residual blocks for contextual feature extraction. Three dimensional convolutional layers are used as basic layers for simultaneous extraction and fusion of polarimetric information and correlation among neighbouring pixels in local regions. The shortcut connections are utilised to overcome the degradation problem due to increasing network depth. In addition, batch normalisation is applied to regularise the learning process. The experimental results on four real POLSAR images show the superior performance of SPRN compared to several state‐of‐the‐art classifiers in terms of various assessment measures.

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Response of the Northwest Indian Ocean purpleback flying squid (Sthenoteuthis oualaniensis) fishing grounds to marine environmental changes and its prediction model construction based on multi-models and multi-spatial and temporal scales

Shi

Jiang

et al. 2023

Ecological Indicators

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Convolutional Kernel‐based covariance descriptor for classification of polarimetric synthetic aperture radar images

Cited by 3 publications

References 33 publications

Lie Group Equivariant Convolutional Neural Network Based on Laplace Distribution

Lie Group Equivariant Convolutional Neural Network Based on Laplace Distribution

Spatial and polarimetric information fusion using residual network for polarimetric synthetic aperture radar image classification

Response of the Northwest Indian Ocean purpleback flying squid (Sthenoteuthis oualaniensis) fishing grounds to marine environmental changes and its prediction model construction based on multi-models and multi-spatial and temporal scales

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