PolSAR Image Classification With Multiscale Superpixel-Based Graph Convolutional Network

Hyperspectral cross-scene classification utilizes the prior knowledge of source scenes with known labels to classify unlabeled target scenes via transfer learning. The existing methods did not properly balance the contribution of marginal and conditional distribution to transfer learning. They did not fully exploit the neighborhood information of intraclass/interclass in the shared transfer subspace. Regarding the two problems, first, by using maximum mean discrepancy and class weights, a direct estimation method was proposed for the balance parameter that adjusted the contribution of the marginal and conditional distribution. It could avoid the inefficiency problem of the traditional indirect estimation method, without introducing extra parameters. Two different strategies (supervised and unsupervised) were developed to comprehensively represent the feature relationship. The difference between source data and the target data was significantly reduced. At the same time, the intraclass and interclass relationships, represented by graph construction in the original high-dimensional space under graph-embedding framework, were maximally maintained. Experimental results on two classical cross-scene data sets (HyRank and Pavia) showed that the proposed method performed more efficiently and accurately in estimating the balance parameter and achieved better cross-scene classification performance by integrating with the graph construction strategies.

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“…where λ is the regularization parameter. X can be obtained by (5), which denotes the input data. M 0 and M c are MMD matrices…”

Section: B Balanced Distribution Adaptationmentioning

confidence: 99%

Graph-Embedding Balanced Transfer Subspace Learning for Hyperspectral Cross-Scene Classification

Zhou

Chen

Liu

et al. 2022

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

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“…Although p c =y is implemented to calculate the probability distribution, (8) does not make detailed distinctions on other category attributes of the corresponding sample.…”

Section: Probability-aware Distributionmentioning

confidence: 99%

DNN-Based PolSAR Image Classification on Noisy Labels

Xiang

Lin

et al. 2022

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

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Deep neural networks (DNNs) appear to be a solution for the classification of polarimetric synthetic aperture radar (Pol-SAR) data in that they outperform classical supervised classifiers under the condition of sufficient training samples. The design of a classifier is challenging because DNNs can easily overfit due to limited remote sensing training samples and unavoidable noisy labels. In this article, a softmax loss strategy with antinoise capability, namely, the probability-aware sample grading strategy (PASGS), is developed to overcome this limitation. Combined with the proposed softmax loss strategy, two classical DNN-based classifiers are implemented to perform PolSAR image classification to demonstrate its effectiveness. In this framework, the difference distribution implicitly reflects the probability that a training sample is clean, and clean labels can be distinguished from noisy labels according to the method of probability statistics. Then, this probability is employed to reweight the corresponding loss of each training sample during the training process to locate the noisy data and to prevent participation in the loss calculation of the neural network. As the number of training iterations increases, the condition of the probability statistics of the noisy labels will be constantly adjusted without supervision, and the clean labels will eventually be identified to train the neural network. Experiments on three PolSAR datasets with two DNN-based methods also demonstrate that the proposed method is superior to state-of-the-art methods.

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“…In addition, there were some other deep networks used in the classification of PolSAR image, such as 3D-CNN combined with the conditional random field [30], CNN combined with graph [31], polarimatric convolutional network [32], Wishart deep stacking network S [33], Wishart autoencoder (Wishart-AE) [34], Wishart convolutional autoencoder (Wishart-CAE) [34], and the improved deep stacked network [35], [36]. Because some superpixel segmentation methods were proved to be effective in preserving spatial structure information of PolSAR images [37]- [39], the superpixel-based graph convolutional network was also proposed for PolSAR image classification [40]. Among the above classification methods based on deep learning, some of them were pixel-by-pixel classification methods.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, there were also a few region-based classification methods which could effectively retain the whole region of the target. In these methods, regions were generated by superpixel segmentation [23], [40] or other methods [12], and then the obtained target regions were classified.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Complex-Valued DeepLabv3+ for Semantic Segmentation of PolSAR Image

Zeng

Liu

et al. 2022

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

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Semantic image segmentation is one kind of end-to-end segmentation methods which can classify the target region pixel by pixel. As a classic semantic segmentation network in optical images, DeepLabv3+ can achieve a good segmentation performance. However, when this network is directly used in the semantic segmentation of polarimetric synthetic aperture radar (PolSAR) image, it is hard to obtain the ideal segmentation results. The reason is that it is easy to yield overfitting due to the small PolSAR dataset. In this paper, a lightweight complex-valued DeepLabv3+ (L-CV-DeepLabv3+) is proposed for semantic segmentation of PolSAR data. It has two significant advantages, when compared with the original DeepLabv3+. First, the proposed network with the simplified structure and parameters can be suitable for the small PolSAR data, and thus it can effectively avoid the overfitting. Second, the proposed complex-valued (CV) network can make full use of both amplitude and phase information of PolSAR data, which brings better segmentation performance than the real-valued (RV) network, and the related CV operations are strictly true in the mathematical sense. Experimental results about two Flevoland datasets and one San Francisco dataset show that the proposed network can obtain better overall average (OA), mean intersection over union (MIOU), and mean pixel accuracy (MPA) than the original DeepLabv3+ and some other RV semantic segmentation networks.

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PolSAR Image Classification With Multiscale Superpixel-Based Graph Convolutional Network

Cited by 35 publications

References 37 publications

Graph-Embedding Balanced Transfer Subspace Learning for Hyperspectral Cross-Scene Classification

Graph-Embedding Balanced Transfer Subspace Learning for Hyperspectral Cross-Scene Classification

DNN-Based PolSAR Image Classification on Noisy Labels

A Lightweight Complex-Valued DeepLabv3+ for Semantic Segmentation of PolSAR Image

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