Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence
Abstract Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled sa...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-09871-w |
| _version_ | 1811290395293777920 |
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| author | Maryam Imani |
| author_facet | Maryam Imani |
| author_sort | Maryam Imani |
| collection | DOAJ |
| description | Abstract Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled samples, which are not usually available in practice, or they cause a high computational burden for implementation. In this work, instead of spending cost for network training, the inherent nature of PolSAR image is used for generation of convolutional kernels for extraction of deep and robust features. Moreover, extraction of diverse scattering characteristics contained in the coherency matrix of PolSAR and fusion of their output classification results with a high confidence have high impact in providing a reliable classification map. The introduced method called discriminative features based high confidence classification (DFC) utilizes several approaches to deal with difficulties of PolSAR image classification. It uses a multi-view analysis to generate diverse classification maps with different information. It extracts deep polarimetric-spatial features, consistent and robust with respect to the original PolSAR data, by applying several pre-determined convolutional filters selected from the important regions of image. Convolutional kernels are fixed without requirement to be learned. The important regions are determined with selecting the key points of image. In addition, a two-step discriminant analysis method is proposed to reduce dimensionality and result in a feature space with minimum overlapping and maximum class separability. Eventually, a high confidence decision fusion is implemented to find the final classification map. Impact of multi-view analysis, selection of important regions as fixed convolutional kernels, two-step discriminant analysis and high confidence decision fusion are individually assessed on three real PolSAR images in different sizes of training sets. For example, the proposed method achieves 96.40% and 98.72% overall classification accuracy by using 10 and 100 training samples per class, respectively in L-band Flevoland image acquired by AIRSAR. Generally, the experiments show high efficiency of DFC compared to several state-of-the-art methods especially for small sample size situations. |
| first_indexed | 2024-04-13T04:12:08Z |
| format | Article |
| id | doaj.art-4e85c0db8202467a955a246e39a5477b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T04:12:08Z |
| publishDate | 2022-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4e85c0db8202467a955a246e39a5477b2022-12-22T03:03:04ZengNature PortfolioScientific Reports2045-23222022-04-0112111310.1038/s41598-022-09871-wTwo-step discriminant analysis based multi-view polarimetric SAR image classification with high confidenceMaryam Imani0Faculty of Electrical and Computer Engineering, Tarbiat Modares UniversityAbstract Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled samples, which are not usually available in practice, or they cause a high computational burden for implementation. In this work, instead of spending cost for network training, the inherent nature of PolSAR image is used for generation of convolutional kernels for extraction of deep and robust features. Moreover, extraction of diverse scattering characteristics contained in the coherency matrix of PolSAR and fusion of their output classification results with a high confidence have high impact in providing a reliable classification map. The introduced method called discriminative features based high confidence classification (DFC) utilizes several approaches to deal with difficulties of PolSAR image classification. It uses a multi-view analysis to generate diverse classification maps with different information. It extracts deep polarimetric-spatial features, consistent and robust with respect to the original PolSAR data, by applying several pre-determined convolutional filters selected from the important regions of image. Convolutional kernels are fixed without requirement to be learned. The important regions are determined with selecting the key points of image. In addition, a two-step discriminant analysis method is proposed to reduce dimensionality and result in a feature space with minimum overlapping and maximum class separability. Eventually, a high confidence decision fusion is implemented to find the final classification map. Impact of multi-view analysis, selection of important regions as fixed convolutional kernels, two-step discriminant analysis and high confidence decision fusion are individually assessed on three real PolSAR images in different sizes of training sets. For example, the proposed method achieves 96.40% and 98.72% overall classification accuracy by using 10 and 100 training samples per class, respectively in L-band Flevoland image acquired by AIRSAR. Generally, the experiments show high efficiency of DFC compared to several state-of-the-art methods especially for small sample size situations.https://doi.org/10.1038/s41598-022-09871-w |
| spellingShingle | Maryam Imani Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence Scientific Reports |
| title | Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence |
| title_full | Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence |
| title_fullStr | Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence |
| title_full_unstemmed | Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence |
| title_short | Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence |
| title_sort | two step discriminant analysis based multi view polarimetric sar image classification with high confidence |
| url | https://doi.org/10.1038/s41598-022-09871-w |
| work_keys_str_mv | AT maryamimani twostepdiscriminantanalysisbasedmultiviewpolarimetricsarimageclassificationwithhighconfidence |