Polarimetric SAR image classification based on target decomposition theorem and complex Wishart distribution

Du, Li-Jen; Lee, J.S.

doi:10.1109/igarss.1996.516366

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…To demonstrate the superiority of the proposed method, we compare it here with other classical and state-of-art methods, including the classical maximum likelihood classifier based on Wishart distance [32] (denoted as Wishart-ML), the Laplacian Eigenmaps and nonlinear dimensionality for representation [33] (denoted as LE-NDR), the D-KSVD model based on an NSCT-domain [16] (denoted as ND-KSVD) and the SVM model based on Riemannian sparse coding [18] (denoted as RSC-SVM).…”

Section: Evaluation On Flevoland-1989mentioning

confidence: 99%

Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

Zhang

Lai

Xie³

et al. 2021

Remote Sensing

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In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification. We made an optimization model for geometry-aware discrimination dictionary learning in which the dictionary learning (GADDL) is generalized from Euclidian space to Riemannian manifolds, and dictionary atoms are composed of manifold data. An efficient optimization algorithm based on an alternating direction multiplier method was developed to solve the model. Experiments were implemented on three public datasets: Flevoland-1989, San Francisco and Flevoland-1991. The experimental results show that the proposed method learned a discriminative dictionary with accuracies better those of comparative methods. The convergence of the model and the robustness of the initial dictionary were also verified through experiments.

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Section: Evaluation On Flevoland-1989mentioning

confidence: 99%

Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

Zhang

Lai

Xie³

et al. 2021

Remote Sensing

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“…There are over 3,500 articles with keywords "SAR Classification" on the IEEE Xplore website, consisting of two majority research topics: Target Classification and Terrain Classification. Methods on SAR Terrain Classification have been developed through three main stages since 1980s [13][14][15], based on mathematics decomposition [16][17][18][19][20][21][22][23], pattern recognition [24][25][26][27][28], and artificial intelligence [29]. The year 2012 was a significant year for every science domain since Hinton first introduced deep learning technology [30].…”

Section: Introductionmentioning

confidence: 99%

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

Zhu¹

2021

IEEE Access

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The dark area in the SAR image corresponds to the zero-energy electromagnetic echo region because specular reflection occurs when the emitted electromagnetic wave (EMW) comes into contact with a smooth object on the ground. In addition, EWM sometimes cannot measure the entire portion of the ground object due to the incident angle of the actual aperture radar sensor and the height of the object on the ground, which creates a dark area in the SAR image called a SAR shadow. Therefore, it is challenging to distinguish the dark area from shadows or some ground objects with smooth surfaces. To address this problem, this paper proposes a novel deep marginal learning algorithm (DMLA) to implement the dark zone classification of airborne SAR images. Specifically, a novel Snake Search Algorithm (SSA) with a chain code is deployed to generate margin slices of dark areas in SAR images, which is used as the input to the following classification network. In addition, a deep convolutional neural network with simple architecture is constructed to train the margin slices of the SSA, which outputs the final classification results with labels. Finally, experiments on three public airborne SAR image datasets output the average classification accuracy of 98.775% for the dark margin slices and an accuracy of 100% to identify dark regions in the original SAR images. The proposed method not only contributes to a high classification accuracy but also improves time efficiency.

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“…According to whether there exists data label and manual intervention, the classification methods can be mainly divided into two types, supervised and unsupervised methods. The unsupervised classification methods classify data according to their statistical characteristics without prior knowledge, such as complex Wishart [8]- [10], k-means clustering [11], [12], fuzzy c-means clustering [13], Expectation Maximization [14] and so on. The unsupervised classification methods cannot obtain satisfactory classification accuracy, when the difference in scattering characteristics of targets is small.…”

Section: Introductionmentioning

confidence: 99%

Interpretable POLSAR Image Classification Based on Adaptive-Dimension Feature Space Decision Tree

et al. 2020

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Decision tree method has been applied to POLSAR image classification, due to its capability to interpret the scattering characteristics as well as good classification accuracy. Compared with popular machine learning classifiers, decision tree approach can explain the scattering process of certain type of targets by use of the polarimetric features at the tree nodes. Except the interpretability, decision tree approach could be transplanted to other data set without training process for the same terrain types, since the polarimetric features are inherently connected to the physical scattering properties. Currently, decision tree based classifiers, typically employ one single polarimetric feature at the nodes of the tree. The idea to increase the number of the polarization features at the decision tree node is expected to improve the classification result, which combine two or more polarimetric features to form a two or high dimension feature space. In this way, the classes which cannot be discriminated with one feature could possibly be separated with the space constructed by several features. However, it also inevitably leads to an increase in the computational burden. In fact, not all nodes require very high-dimensional feature space to achieve high classification precision. Therefore, in this paper we proposed that the dimension of the feature space used in the decision tree nodes is adaptively changed from one to three, due to the separability of the classes under this node. The developed classification method is examined by the classical AIRSAR data in Flevoland area of the Netherlands, as well as GaoFen-3 data in Hulunbuir of China. The experiments show that the classification performance is superior to the fixed dimension feature decision tree methods, with less and reasonable computation time. Besides, the transferability of polarimetric features obtained by decision tree is preliminarily demonstrated in the application to another AIRSAR data.

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Polarimetric SAR image classification based on target decomposition theorem and complex Wishart distribution

Cited by 11 publications

References 7 publications

Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

Interpretable POLSAR Image Classification Based on Adaptive-Dimension Feature Space Decision Tree

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