POLSAR Image Classification via Wishart-AE Model or Wishart-CAE Model

Xie, Wen; Jiao, Licheng; Hou, Biao; Ma, Wenping; Zhao, Jin; Shuyin, Zhang; Liu, Fang

doi:10.1109/jstars.2017.2698076

Cited by 71 publications

(35 citation statements)

References 32 publications

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“…where i h denotes the i -th one-look sample, and H represents the Hermitian transpose. From [38], we can derive the coherency matrix T from Equation (21).…”

Section: Input Feature Preparationmentioning

confidence: 99%

Classification of PolSAR Image Using Neural Nonlocal Stacked Sparse Autoencoders with Virtual Adversarial Regularization

Wang

2019

Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) has become increasingly popular in the past two decades, for it can derive multichannel features of ground objects, which contains more discriminative information compared with traditional SAR. In this paper, a neural nonlocal stacked sparse autoencoders with virtual adversarial regularization (NNSSAE-VAT) is proposed for PolSAR image classification. The NNSSAE first extracts the nonlocal features by calculating pairwise similarity of each pixel and its surrounding pixels using a neural network, which contains a multiscale feature extractor and a linear embedding layer. The feature extraction process can relieve the negative influence of speckle noise and extract discriminative nonlocal spatial information without carefully designed parameters. Then, the SSAE maps the center pixel and the extracted nonlocal features into deep latent space in which a Softmax classifier is utilized to conduct classification. The virtual adversarial training is introduced to regularize the network, which tries to keep the network from being overfitting. The experimental results from three real PolSAR image show that the proposed NNSSAE-VAT method has proved its robustness and effectiveness and it can achieve competitive performance compared with related methods.

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“…where i h denotes the i -th one-look sample, and H represents the Hermitian transpose. From [38], we can derive the coherency matrix T from Equation (21).…”

Section: Input Feature Preparationmentioning

confidence: 99%

Classification of PolSAR Image Using Neural Nonlocal Stacked Sparse Autoencoders with Virtual Adversarial Regularization

Wang

2019

Remote Sensing

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“…In [35], a new type of restricted boltzmann machine (RBM) is specially defined, which we name the Wishart-Bernoulli RBM (WBRBM), and is used to form a deep network named as Wishart Deep Belief Networks (W-DBN). In [65], a new type of autoencoder (AE) and convolutional autoencoder (CAE) is specially defined, which we name them Wishart-AE (WAE) and Wishart-CAE (WCAE). In [66], a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Convolutional Network for PolSAR Image Classification

Liu

Jiao

Tang

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix are used as the main research object to extract features. In this paper, we focus on the original polarimetric scattering matrix and propose a polarimetric scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on convolution network to combine this feature. Based on polarimetric scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for polarimetric scattering coding is available at https://github.com/liuxuvip/Polarimetric-Scattering-Coding.

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“…In particular, the convolutional neural network (CNN) model, based on convolution, pooling and nonlinear transformation operations, has achieved great performance in image classification [32], semantic segmentation [33], scene labeling [34], action recognition [35] and object detection [36,37]. Because of the great performance of the CNN model, it has also been successfully into PolSAR image classification [38][39][40][41][42][43]. However, CNN still has a few shortcomings in PolSAR image classification.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the input is an image of a cat, CNN can get the class of the image is cat, but it cannot tell which part of the image is cat's leg and which part is cat's face. Therefore, for PolSAR image classification, the neighborhood of a pixel is set as the input to get the class of the pixel [38][39][40][41][42][43]. For instance, recording one pixel in the image as p 1 , the neighborhood of p 1 is set as the input to get the class of p 1 .…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Fully Convolutional Network for PolSAR Image Classification

Chen

Liu

et al. 2018

Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR image classification. However, there are some problems to be solved in PolSAR image classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR image classification. The merit of our method is twofold:(1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the image integrity can be maintained in the maximum extent. We use three PolSAR images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR image classification.Keywords: polarimetric synthetic aperture radar (PolSAR); fully convolutional network (FCN); sliding window fully convolutional network and sparse coding (SFCN-SC); convolutional neural network (CNN)

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POLSAR Image Classification via Wishart-AE Model or Wishart-CAE Model

Cited by 71 publications

References 32 publications

Classification of PolSAR Image Using Neural Nonlocal Stacked Sparse Autoencoders with Virtual Adversarial Regularization

Classification of PolSAR Image Using Neural Nonlocal Stacked Sparse Autoencoders with Virtual Adversarial Regularization

Polarimetric Convolutional Network for PolSAR Image Classification

A Novel Deep Fully Convolutional Network for PolSAR Image Classification

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