A New Multispectral Data Augmentation Technique Based on Data Imputation
Deep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters they need to learn. The high density of inform...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/23/4875 |
| _version_ | 1797507207546273792 |
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| author | Álvaro Acción Francisco Argüello Dora B. Heras |
| author_facet | Álvaro Acción Francisco Argüello Dora B. Heras |
| author_sort | Álvaro Acción |
| collection | DOAJ |
| description | Deep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters they need to learn. The high density of information present in remote sensing imagery with high spectral resolution can make the application of DL models to large scenes challenging. Methods such as patch-based classification require large amounts of data to be processed during the training and prediction stages, which translates into long processing times and high energy consumption. One of the solutions to decrease the computational cost of these models is to perform segment-based classification. Segment-based classification schemes can significantly decrease training and prediction times, and also offer advantages over simply reducing the size of the training datasets by randomly sampling training data. The lack of a large enough number of samples can, however, pose an additional challenge, causing these models to not generalize properly. Data augmentation methods are used to generate new synthetic samples based on existing data to increase the classification performance. In this work, we propose a new data augmentation scheme using data imputation and matrix completion methods for segment-based classification. The proposal has been validated using two high-resolution multispectral datasets from the literature. The results obtained show that the proposed approach successfully increases the classification performance across all the scenes tested and that data imputation methods applied to multispectral imagery are a valid means to perform data augmentation. A comparison of classification accuracy between different imputation methods applied to the proposed scheme was also carried out. |
| first_indexed | 2024-03-10T04:46:16Z |
| format | Article |
| id | doaj.art-38236ba7abde45cc944209450a4a21c4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T04:46:16Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-38236ba7abde45cc944209450a4a21c42023-11-23T02:57:43ZengMDPI AGRemote Sensing2072-42922021-11-011323487510.3390/rs13234875A New Multispectral Data Augmentation Technique Based on Data ImputationÁlvaro Acción0Francisco Argüello1Dora B. Heras2Centro Singular de Investigación en Tecnologías Inteligentes, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, SpainDepartamento de Electrónica y Computación, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, SpainCentro Singular de Investigación en Tecnologías Inteligentes, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, SpainDeep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters they need to learn. The high density of information present in remote sensing imagery with high spectral resolution can make the application of DL models to large scenes challenging. Methods such as patch-based classification require large amounts of data to be processed during the training and prediction stages, which translates into long processing times and high energy consumption. One of the solutions to decrease the computational cost of these models is to perform segment-based classification. Segment-based classification schemes can significantly decrease training and prediction times, and also offer advantages over simply reducing the size of the training datasets by randomly sampling training data. The lack of a large enough number of samples can, however, pose an additional challenge, causing these models to not generalize properly. Data augmentation methods are used to generate new synthetic samples based on existing data to increase the classification performance. In this work, we propose a new data augmentation scheme using data imputation and matrix completion methods for segment-based classification. The proposal has been validated using two high-resolution multispectral datasets from the literature. The results obtained show that the proposed approach successfully increases the classification performance across all the scenes tested and that data imputation methods applied to multispectral imagery are a valid means to perform data augmentation. A comparison of classification accuracy between different imputation methods applied to the proposed scheme was also carried out.https://www.mdpi.com/2072-4292/13/23/4875multispectralclassificationdeep learningCNNsegmentationdata augmentation |
| spellingShingle | Álvaro Acción Francisco Argüello Dora B. Heras A New Multispectral Data Augmentation Technique Based on Data Imputation Remote Sensing multispectral classification deep learning CNN segmentation data augmentation |
| title | A New Multispectral Data Augmentation Technique Based on Data Imputation |
| title_full | A New Multispectral Data Augmentation Technique Based on Data Imputation |
| title_fullStr | A New Multispectral Data Augmentation Technique Based on Data Imputation |
| title_full_unstemmed | A New Multispectral Data Augmentation Technique Based on Data Imputation |
| title_short | A New Multispectral Data Augmentation Technique Based on Data Imputation |
| title_sort | new multispectral data augmentation technique based on data imputation |
| topic | multispectral classification deep learning CNN segmentation data augmentation |
| url | https://www.mdpi.com/2072-4292/13/23/4875 |
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