Feature extraction and classification of PolSAR images based on sparse representation

Zhang, Lamei; Sun, Liangjie; Moon, Wooil M.

doi:10.1109/igarss.2014.6947057

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…For example, in view of the problem of class distribution bias in datasets between different domains, an unsupervised domain adaptive network based on coordinated attention and weighted clustering was presented for achieving the alignment of data distributions between different domains [19]. In the last decade, the use of machine learning methods for PolSAR image classification tasks has become a major trend [20]- [22]. Meanwhile, the link between machine learning and statistical data distribution methods is also becoming a topic in research.…”

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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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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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A Multi-decision Fusion Strategy for PolSAR Image Classification

Song,

Chen,

Wang

et al. 2024

Proceedings of the 2024 7th International Conference on Data Storage and Data Engineering

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Land Cover Classification Based on Double Scatterer Model and Neural Networks

Karachristos

Anastassopoulos

2022

Geomatics

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In this paper, a supervised land cover classification is presented based on the extracted information from polarimetric synthetic aperture radar (PolSAR) images. The analysis of the polarimetric scattering matrix is accomplished according to the Double Scatterer Model which interprets each PolSAR cell by a pair of elementary scattering mechanisms. Subsequently, by utilizing the contribution rate of the two fundamental scatterers, a novel data representation is accomplished, providing great informational content. The main component of the research is to highlight the robust new feature-tool and afterwards to present a classification scheme exploiting a fully connected artificial neural network (ANN). The PolSAR images used to verify the proposed method were acquired by RADARSAT-2 and the experimental results confirm the effectiveness of the presented methodology with an overall classification accuracy of 93%, which is considered satisfactory since only four feature-vectors are used.

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Feature extraction and classification of PolSAR images based on sparse representation

Cited by 3 publications

References 13 publications

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

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

A Multi-decision Fusion Strategy for PolSAR Image Classification

Land Cover Classification Based on Double Scatterer Model and Neural Networks

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