Semisupervised PolSAR Image Classification Based on Improved Cotraining

Hua, Wenqiang; Wang, Shuang; Liu, Hongying; Liu, Kun; Guo, Yanhe; Jiao, Licheng

doi:10.1109/jstars.2017.2728067

Cited by 20 publications

(11 citation statements)

References 37 publications

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“…Consequently, the semi-supervised methods can not only use the information of labeled samples, but also utilize the information of the unlabeled ones to improve the classification performance [ 20 ]. Moreover, in practice, it is feasible to obtain a larger number of unlabeled PolSAR images, which makes the semi-supervised methods more attractive for solving the problem of PolSAR image classification with limited labeled samples [ 14 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Classification of PolSAR Images Based on Co-Training of CNN and SVM with Limited Labeled Samples

Zhao

Cheng

Qin

et al. 2023

Sensors

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Recently, convolutional neural networks (CNNs) have shown significant advantages in the tasks of image classification; however, these usually require a large number of labeled samples for training. In practice, it is difficult and costly to obtain sufficient labeled samples of polarimetric synthetic aperture radar (PolSAR) images. To address this problem, we propose a novel semi-supervised classification method for PolSAR images in this paper, using the co-training of CNN and a support vector machine (SVM). In our co-training method, an eight-layer CNN with residual network (ResNet) architecture is designed as the primary classifier, and an SVM is used as the auxiliary classifier. In particular, the SVM is used to enhance the performance of our algorithm in the case of limited labeled samples. In our method, more and more pseudo-labeled samples are iteratively yielded for training through a two-stage co-training of CNN and SVM, which gradually improves the performance of the two classifiers. The trained CNN is employed as the final classifier due to its strong classification capability with enough samples. We carried out experiments on two C-band airborne PolSAR images acquired by the AIRSAR systems and an L-band spaceborne PolSAR image acquired by the GaoFen-3 system. The experimental results demonstrate that the proposed method can effectively integrate the complementary advantages of SVM and CNN, providing overall classification accuracy of more than 97%, 96% and 93% with limited labeled samples (10 samples per class) for the above three images, respectively, which is superior to the state-of-the-art semi-supervised methods for PolSAR image classification.

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Section: Introductionmentioning

confidence: 99%

Semi-Supervised Classification of PolSAR Images Based on Co-Training of CNN and SVM with Limited Labeled Samples

Zhao

Cheng

Qin

et al. 2023

Sensors

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“…Semi-supervised classification utilizes unlabeled samples to expand training sets, which greatly contributes towards overcoming over-fitting and achieving a better classification accuracy. In general, semi-supervised classification can be divided into five categories, including generation models [24], self-training models [25], co-training models [26], tranductive SVMs [27], and graph-based models [28]. Among all the semi-supervised models, the self-training model, the co-training model and the graph-based model are the most frequently used in PolSAR classification.…”

Section: Introductionmentioning

confidence: 99%

“…Among all the semi-supervised models, the self-training model, the co-training model and the graph-based model are the most frequently used in PolSAR classification. A semi-supervised PolSAR classification method based on improved co-training has been proposed in the literature [26]. Two sufficient and conditionally independent views were constructed for the co-training process based on features of PolSAR images, and a novel sample selection strategy for selecting reliable unlabeled samples is used to update this process.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

Xing

Jiao

et al. 2019

Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

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“…Therefore, PolSAR image classification has attracted extensive research attention and more and more methods are proposed to accomplish this task. The majority of available methods are based on feature extraction and representation [7]- [9], and classifier designing and optimization [10]- [12]. In general, polarimetric target decomposition is one of most powerful and widely used methods for feature extraction of PolSAR images, such as Krogager decomposition [13], Pauli decomposition [14], H/a/A decomposition [15], Freeman three-component decomposition [16], and Huynen decomposition [17], Yamaguchi decomposition [18], and the extensions of the these mentioned methods [19], [20].…”

mentioning

confidence: 99%

Polarimetric SAR Image Classification Based on Ensemble Dual-Branch CNN and Superpixel Algorithm

Hua

Zhang

Xie

et al. 2022

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

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Recently, convolutional neural networks (CNNs) have been successfully utilized in polarimetric synthetic aperture radar (PolSAR) image classification and obtained promising results. However, most CNN-based classification methods require a large number of labeled samples and it is difficult to obtain sufficient labeled samples. For this reason, an ensemble dual-branch CNN (EDb-CNN) is proposed for PolSAR image classification with small samples. First, to solve the problem of the small sample in Pol-SAR image classification, a new data enhancement method based on the superpixel algorithm is proposed to expand the number of labeled samples. Second, to obtain different scales of features from PolSAR images, a Db-CNN model is proposed. This model contains two parallel CNN structures. One CNN branch is used to extract the polarization features from the complex coherency matrix. The other branch is utilized to extract the spatial features based on weighted spatial neighborhood. On the top of these two branches, a feature fusion model is adopted to combine these two deep features, and a weighted loss function is employed to improve the learning procedure. Then, the ensemble learning algorithm is used for each CNN branch and Db-CNN network to obtain the better classification results. Finally, a postprocess algorithm based on the superpixel algorithm is proposed to improve the consistency of classification results. Experiments on two PolSAR datasets show that the proposed method achieves a much better performance than other classification methods, especially when only a few labeled samples are available.

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Semisupervised PolSAR Image Classification Based on Improved Cotraining

Cited by 20 publications

References 37 publications

Semi-Supervised Classification of PolSAR Images Based on Co-Training of CNN and SVM with Limited Labeled Samples

Semi-Supervised Classification of PolSAR Images Based on Co-Training of CNN and SVM with Limited Labeled Samples

Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

Polarimetric SAR Image Classification Based on Ensemble Dual-Branch CNN and Superpixel Algorithm

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