A Deep Reinforcement Learning-Based Framework for PolSAR Imagery Classification

Nie, Wen; Huang, Kui; Li, Pingxiang

doi:10.1109/tgrs.2021.3093474

Cited by 17 publications

(17 citation statements)

References 43 publications

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“…Therefore, this input scheme was used as the basic one. Then, according to references [58][59][60], the correlation coefficients between channels T 12 , T 23 , T 23 , as well as the non-normalized P 0 (NonP 0 ) of the T matrix, were used as the research Scheme 2, where the correlation coefficients between channels are defined by Formulas ( 26)- (28).…”

Section: Input Feature Normalization and Design Of Three Schemesmentioning

confidence: 99%

“…These polarization decomposition algorithms have been applied to PolSAR land cover classification by relevant researchers. Nie et al [28] utilized 12 polarization features obtained from Freeman-Durden decomposition, Van Zyl decomposition, and H/A/Alpha decomposition and achieved good classification results on limited samples using an enhanced learning framework. Wang et al [2] applied the Freeman-Durden decomposition method and used a feature fusion strategy to classify PolSAR images of the Flevoland region.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Classification Scheme for PolSAR Image Based on Polarimetric Features

Zhang,

Cui,

Dong

et al. 2024

Remote Sensing

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Polarimetric features extracted from polarimetric synthetic aperture radar (PolSAR) images contain abundant back-scattering information about objects. Utilizing this information for PolSAR image classification can improve accuracy and enhance object monitoring. In this paper, a deep learning classification method based on polarimetric channel power features for PolSAR is proposed. The distinctive characteristic of this method is that the polarimetric features input into the deep learning network are the power values of polarimetric channels and contain complete polarimetric information. The other two input data schemes are designed to compare the proposed method. The neural network can utilize the extracted polarimetric features to classify images, and the classification accuracy analysis is employed to compare the strengths and weaknesses of the power-based scheme. It is worth mentioning that the polarized characteristics of the data input scheme mentioned in this article have been derived through rigorous mathematical deduction, and each polarimetric feature has a clear physical meaning. By testing different data input schemes on the Gaofen-3 (GF-3) PolSAR image, the experimental results show that the method proposed in this article outperforms existing methods and can improve the accuracy of classification to a certain extent, validating the effectiveness of this method in large-scale area classification.

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Section: Input Feature Normalization and Design Of Three Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Classification Scheme for PolSAR Image Based on Polarimetric Features

Zhang,

Cui,

Dong

et al. 2024

Remote Sensing

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“…Jing et al [26] designed a method that simultaneously utilizes both the self-attention mechanisms of polarized spatial reconstruction networks for solving the classification of similar objects in PolSAR images. Nie et al [27] demonstrated that deep reinforcement learning combined with FCN can achieve higher classification accuracy under limited samples. Yang et al [28] utilized N-clustering generative adversarial networks and deep learning techniques to enhance the accuracy of PolSAR image classification.…”

Section: Introductionmentioning

confidence: 99%

A New Architecture of a Complex-Valued Convolutional Neural Network for PolSAR Image Classification

Ren,

Jiang,

Liu

2023

Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) image classification has been an important area of research due to its wide range of applications. Traditional machine learning methods were insufficient in achieving satisfactory results before the advent of deep learning. Results have significantly improved with the widespread use of deep learning in PolSAR image classification. However, the challenge of reconciling the complex-valued inputs of PolSAR images with the real-valued models of deep learning remains unsolved. Current complex-valued deep learning models treat complex numbers as two distinct real numbers, providing limited assistance in PolSAR image classification results. This paper proposes a novel, complex-valued deep learning approach for PolSAR image classification to address this issue. The approach includes amplitude-based max pooling, complex-valued nonlinear activation, and a cross-entropy loss function based on complex-valued probability. Amplitude-based max pooling reduces computational effort while preserving the most valuable complex-valued features. Complex-valued nonlinear activation maps feature into a high-dimensional complex-domain space, producing the most discriminative features. The complex-valued cross-entropy loss function computes the classification loss using the complex-valued model output and dataset labels, resulting in more accurate and robust classification results. The proposed method was applied to a shallow CNN, deep CNN, FCN, and SegNet, and its effectiveness was verified on three public datasets. The results showed that the method achieved optimal classification results on any model and dataset.

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“…DL has a strong capability to learn a series of abstract hierarchical features from raw input data and obtain a task-specific output. Therefore, DL technology provides an entirely new method for PolSAR image classification, and many classification methods have been proposed, such as Wishart deep belief network (WDBN) [21], Wishart-auto-encode (WAE) [22][23][24], sparse autoencoder [25,26], the long short-term memory (LSTM) network [27], semisupervised deep learning model [28,29], deep reinforcement learning [30], and convolutional neural network (CNN) [31][32][33]. CNN has made impressive achievements in the field of PolSAR image classification among these DL methods, and it consists of several successive convolution layers, pooling layers, and fully-connected layers.…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification

2022

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Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is designed for pixel-to-pixel tasks, has obtained enormous success in semantic segmentation. Therefore, effectively using the FCN model combined with polarimetric characteristics for PolSAR image classification is quite promising. This paper proposes a novel FCN model by adopting complex-valued domain stacked-dilated convolution (CV-SDFCN). Firstly, a stacked-dilated convolution layer with different dilation rates is constructed to capture multi-scale features of PolSAR image; meanwhile, the sharing weight is employed to reduce the calculation burden. Unfortunately, the labeled training samples of PolSAR image are usually limited. Then, the encoder–decoder structure of the original FCN is reconstructed with a U-net model. Finally, in view of the significance of the phase information for PolSAR images, the proposed model is trained in the complex-valued domain rather than the real-valued domain. The experiment results show that the classification performance of the proposed method is better than several state-of-the-art PolSAR image classification methods.

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A Deep Reinforcement Learning-Based Framework for PolSAR Imagery Classification

Cited by 17 publications

References 43 publications

A Deep Learning Classification Scheme for PolSAR Image Based on Polarimetric Features

A Deep Learning Classification Scheme for PolSAR Image Based on Polarimetric Features

A New Architecture of a Complex-Valued Convolutional Neural Network for PolSAR Image Classification

Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification

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