Unified Classification Framework for Multipolarization and Dual-Frequency SAR

Liu, Mingliang; Deng, Yunkai; Wang, Donghong; Liu, Xiuqing; Wang, Chunle

doi:10.1109/tgrs.2023.3242695

Cited by 4 publications

(4 citation statements)

References 62 publications

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“…To validate the effectiveness of the proposed MSSARFNet, comparative experiments were conducted using several representative classification methods, including SVM [13], SSFTT [44], S2FL [17], EndNet [26], CoupledCNN [21], MFT [46], FGCN [28], HCT [31], AMSSE-Net [29], and MACN [48]. To ensure a fair comparison, the network parameters of these methods were set the same as described in their respective articles.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…To address this limitation, Zhao et al [47] designed a novel network which joints convolution and transformer to extract spatialspectral information and achieves effective fusion. Li et al [48] proposed a unified framework that incorporated a multihead cross-modal attention mechanism to capture the interplay between multi-source data and aggregate contextual information. Yao et al [49] proposed a general multimodal transformer framework that designed a hybrid spatial vision transformer backbone implemented with both self-attention and crossmodality attention (CMA) mechanisms for better information fusion in classifying multimodal RS data.…”

Section: B Attention-based Classification: From Single-source To Mult...mentioning

confidence: 99%

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Multiscale Spectral–Spatial Attention Residual Fusion Network for Multisource Remote Sensing Data Classification

Wang,

Liu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The joint of multi-source remote sensing (RS) data for land cover classification has become a popular research topic. Although studies have shown that the fusion of multi-source data can improve the accuracy of classification, the current limitation lies in the inadequate exploitation of information, resulting in spectral confusion categories overlapping, and varying visual differences within the same category. To address these problems, this paper proposes a multi-scale spectralspatial attention residual fusion network (MSSARFNet) that aims to enhance the classification performance of multi-source RS data through effective spectral-spatial feature extraction and fusion. Specifically, three modules are designed: the multiscale spectral attention residual module (MSpeARM), the multiscale spatial attention residual module (MSpaARM) and multiscale convolution fusion (MCF) module. Firstly, we divide the channels of the features into multiple paths and apply spectralspatial attention mechanisms on each path. To further enhance connectivity, convolutional operation is utilized to establish connections between different paths, thus forming MSpeARM and MSpaARM. The MSpeARM suppresses redundant features and enhances effective features along the channel dimension to better differentiate spectral-confused categories. The MSpaARM highlights that different visual patterns of objects in the same category can mutually reinforce each other by weighting all positional features, regardless of their spatial differences. Secondly, to fuse these two sets of features, the MCF module is designed to learn multi-level semantic features and enhance fusion at a granular level. Experimental evaluations on three RS datasets demonstrate that the proposed method achieves excellent classification performance, indicating the effectiveness of MSSARFNet. The source code of the proposed method will be available at https://github.com/wangxu0210/MSSARFNet.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: B Attention-based Classification: From Single-source To Mult...mentioning

confidence: 99%

Multiscale Spectral–Spatial Attention Residual Fusion Network for Multisource Remote Sensing Data Classification

Wang,

Liu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Traditional scattering mechanism-based methods primarily focus on exploiting the scattering features and designing classifiers, which can be categorized into three main groups. The first category comprises statistical distribution-based methods that leverage the statistical characteristics of PolSAR complex matrix data, such as Wishart [6][7][8][9], mixed Wishart [10][11][12][13], G0 [14], Kummer [15] distribution. These methods try to exploit various non-Gaussian distribution models for heterogeneous PolSAR images.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Synthetic Aperture Radar Image Classification Based on Double-Channel Convolution Network and Edge-Preserving Markov Random Field

Shi,

Nie,

et al. 2023

Remote Sensing

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Deep learning methods have gained significant popularity in the field of polarimetric synthetic aperture radar (PolSAR) image classification. These methods aim to extract high-level semantic features from the original PolSAR data to learn the polarimetric information. However, using only original data, these methods cannot learn multiple scattering features and complex structures for extremely heterogeneous terrain objects. In addition, deep learning methods always cause edge confusion due to the high-level features. To overcome these limitations, we propose a novel approach that combines a new double-channel convolutional neural network (CNN) with an edge-preserving Markov random field (MRF) model for PolSAR image classification, abbreviated to “DCCNN-MRF”. Firstly, a double-channel convolution network (DCCNN) is developed to combine complex matrix data and multiple scattering features. The DCCNN consists of two subnetworks: a Wishart-based complex matrix network and a multi-feature network. The Wishart-based complex matrix network focuses on learning the statistical characteristics and channel correlation, and the multi-feature network is designed to learn high-level semantic features well. Then, a unified network framework is designed to fuse two kinds of weighted features in order to enhance advantageous features and reduce redundant ones. Finally, an edge-preserving MRF model is integrated with the DCCNN network. In the MRF model, a sketch map-based edge energy function is designed by defining an adaptive weighted neighborhood for edge pixels. Experiments were conducted on four real PolSAR datasets with different sensors and bands. The experimental results demonstrate the effectiveness of the proposed DCCNN-MRF method.

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“…Traditional scattering mechanism-based methods primarily focus on exploiting the scattering features and designing classifier, which can be categorized into three main groups. The first category comprises statistical distribution-based methods that leverage the statistical characteristics of PolSAR complex matrix data, such as Wishart [6][7][8][9], mixed Wishart [10][11][12][13], G0 [14], Kummer [15] distributions.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric SAR Image Classification Based on Double-Channel Convolution Network and Edge-preserving Markov Random Field

Shi,

Nie,

et al. 2023

Preprint

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Deep learning methods have been widely used in PolSAR image classification. To learn the polarimetric information, many deep learning methods expect to learn high-level semantic features from original PolSAR data. However, only original data cannot learn multiple scattering features and complex structures for extremely heterogeneous terrain objects. In addition, deep learning methods always cause edge confusion due to the high-level features. To overcome these shortages, we propose a double-channel CNN network combined with an edge-preserving MRF model(DCCNN-MRF) for PolSAR image classification. Firstly, to combine complex matrix data and multiple scattering features together, a double-channel convolution network(DCCNN) is developed, which consists of a Wishart-based complex matrix and multi-feature subnetworks. The Wishart-based complex matrix network can learn the statistical characteristics and channel correlation well, and the multi-feature network can learn high-level semantic features well. Then, a unified network framework is designed to fuse two kinds of features to enhance advantageous features and reduce redundant ones. Finally, an edge preserving MRF model is designed to combine with the DCCNN network. In the MRF model, a sketch map-based edge energy function is designed by defining adaptive weighted neighborhood for edge pixels. Experiments are conducted on four real PolSAR data sets with different sensors and bands. Experimental results demonstrate the effectiveness of the proposed DCCNN-MRF method.

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Unified Classification Framework for Multipolarization and Dual-Frequency SAR

Cited by 4 publications

References 62 publications

Multiscale Spectral–Spatial Attention Residual Fusion Network for Multisource Remote Sensing Data Classification

Multiscale Spectral–Spatial Attention Residual Fusion Network for Multisource Remote Sensing Data Classification

Polarimetric Synthetic Aperture Radar Image Classification Based on Double-Channel Convolution Network and Edge-Preserving Markov Random Field

Polarimetric SAR Image Classification Based on Double-Channel Convolution Network and Edge-preserving Markov Random Field

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