Modeling PolSAR classification using convolutional neural network with homogeneity based kernel selection

Parikh, Hemani; Patel, Samir; Patel, Vibha

doi:10.1007/s40808-023-01700-x

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Compared to traditional SAR technology, PolSAR adopts the multi-channel and multi-polarization working mode, which can obtain rich target information through the transmission and reception of polarimetric electromagnetic waves. Because of these advantages, PolSAR image has made outstanding achievements in remote sensing applications in a variety of fields [1][2][3]. PolSAR image terrain classification is a very important fundamental project in these applications, which aims to classify the pixels of the whole map into the corresponding categories through the polarimetric information.…”

Section: Introductionmentioning

confidence: 99%

PolSAR Image Classification Based on Relation Network with SWANet

Hua

Zhang

et al. 2023

Remote Sensing

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Deep learning and convolutional neural networks (CNN) have been widely applied in polarimetric synthetic aperture radar (PolSAR) image classification, and satisfactory results have been obtained. However, there is one crucial issue that still has not been solved. These methods require abundant labeled samples and obtaining the labeled samples of PolSAR images is usually time-consuming and labor-intensive. To obtain better classification results with fewer labeled samples, a new attention-based 3D residual relation network (3D-ARRN) is proposed for PolSAR image. Firstly, a multilayer CNN with residual structure is used to extract depth polarimetric features. Secondly, to extract more important feature information and improve the classification results, a spatial weighted attention network (SWANet) is introduced to concentrate the feature information, which is more favorable for a classification task. Then, the features of training and test samples are integrated and CNN is utilized to compute the score of similarity between training and test samples. Finally, the similarity score is used to determine the category of test samples. Studies on four different PolSAR datasets illustrate that the proposed 3D-ARRN model can achieve higher classification results than other comparison methods with few labeled data.

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Section: Introductionmentioning

confidence: 99%

PolSAR Image Classification Based on Relation Network with SWANet

Hua

Zhang

et al. 2023

Remote Sensing

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“…The original dataset can be expanded by combining different samples through such a special structure, which relieves the problem of insufficient training data in the PolSAR image classification task. Parikh et al [31] pointed out that fewer studies have explored the effect of convolutional kernel size selection on classification modeling. Hence, a CNN based on homogeneous kernel selection was introduced for PolSAR classification modeling.…”

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confidence: 99%

PolSAR Image Classification Via a Multigranularity Hybrid CNN-ViT Model With External Tokens and Cross-Attention

Wang,

Quan

et al. 2024

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

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With the development of deep learning technology, the application of convolutional neural network (CNN) and vision transformer (ViT) for polarimetric synthetic aperture radar (PolSAR) image classification has been deepened. However, the PolSAR image has very rich information due to its special data form, which makes it difficult for the existing single network structure to comprehensively extract such effective information. In addition, deep learning methods require a large amount of data for training, whereas PolSAR labeled data is scarce and difficult to obtain. Therefore, a multi-granularity hybrid CNN-ViT model based on external tokens and cross-attention is proposed for PolSAR image classification. First of all, CNN is able to learn local features very well. Thus, a CNN-based external feature extractor is designed to extract local features from the PolSAR image. Then, ViT can focus on global features. So, a multi-granularity attention structure is constructed for extracting global information at multiple scales. With these two modules, the model can fully access the feature information contained in PolSAR images, which is more advantageous than a single network structure. Next, to further utilize these features, a crossattention feature fusion module is built for fusing global-local information of different granularities. Finally, by connecting with the softmax classifier, the network outputs the final prediction results. Experimental results on three benchmark datasets show that the present method using a small amount of labeled data for training also achieves the highest level of classification among the compared methods.

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A Survey on Deep Learning for Few-Shot PolSAR Image Classification

Wang,

Jin,

et al. 2024

Remote Sensing

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Few-shot classification of polarimetric synthetic aperture radar (PolSAR) images is a challenging task due to the scarcity of labeled data and the complex scattering properties of PolSAR data. Traditional deep learning models often suffer from overfitting and catastrophic forgetting in such settings. Recent advancements have explored innovative approaches, including data augmentation, transfer learning, meta-learning, and multimodal fusion, to address these limitations. Data augmentation methods enhance the diversity of training samples, with advanced techniques like generative adversarial networks (GANs) generating realistic synthetic data that reflect PolSAR’s polarimetric characteristics. Transfer learning leverages pre-trained models and domain adaptation techniques to improve classification across diverse conditions with minimal labeled samples. Meta-learning enhances model adaptability by learning generalizable representations from limited data. Multimodal methods integrate complementary data sources, such as optical imagery, to enrich feature representation. This survey provides a comprehensive review of these strategies, focusing on their advantages, limitations, and potential applications in PolSAR classification. We also identify key trends, such as the increasing role of hybrid models combining multiple paradigms and the growing emphasis on explainability and domain-specific customization. By synthesizing SOTA approaches, this survey offers insights into future directions for advancing few-shot PolSAR classification.

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Modeling PolSAR classification using convolutional neural network with homogeneity based kernel selection

Cited by 3 publications

References 31 publications

PolSAR Image Classification Based on Relation Network with SWANet

PolSAR Image Classification Based on Relation Network with SWANet

PolSAR Image Classification Via a Multigranularity Hybrid CNN-ViT Model With External Tokens and Cross-Attention

A Survey on Deep Learning for Few-Shot PolSAR Image Classification

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