1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPEW34M/49L85TH |
Repository | sid.inpe.br/sibgrapi/2023/08.16.17.04 |
Last Update | 2023:08.16.17.04.58 (UTC) crjung@inf.ufrgs.br |
Metadata Repository | sid.inpe.br/sibgrapi/2023/08.16.17.04.58 |
Metadata Last Update | 2024:02.17.04.05.20 (UTC) administrator |
DOI | 10.1109/SIBGRAPI59091.2023.10347038 |
Citation Key | Albuquerque:2023:MuImSe |
Title | Multispectral Image Segmentation With Dimensionality Reduction Using Autoencoders |
Short Title | Multispectral Image Segmentation With Dimensionality Reduction Using Autoencoders |
Format | On-line |
Year | 2023 |
Access Date | 2024, May 11 |
Number of Files | 1 |
Size | 408 KiB |
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2. Context | |
Author | Albuquerque, Eliton |
Affiliation | Federal University of Rio Grande do Sul |
Editor | Clua, Esteban Walter Gonzalez Körting, Thales Sehn Paulovich, Fernando Vieira Feris, Rogerio |
e-Mail Address | jeafilho@inf.ufrgs.br |
Conference Name | Conference on Graphics, Patterns and Images, 36 (SIBGRAPI) |
Conference Location | Rio Grande, RS |
Date | Nov. 06-09, 2023 |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2023-08-16 17:04:58 :: crjung@inf.ufrgs.br -> administrator :: 2024-02-17 04:05:20 :: administrator -> crjung@inf.ufrgs.br :: 2023 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Keywords | multispectral image processing semantic segmentation dimensionality reduction |
Abstract | Autoencoder (AE) implementations through neural networks have achieved impressive results on dimensionality reduction tasks, such as multispectral (MS) imagery compression. Dimensionality reduction algorithms are necessary when dealing with large multispectral datasets, since the data captured by mul- tiple levels of narrow spectral wavelengths causes high processing and storage costs, particularly when such highly dimensional MS data are used as input to deep learning networks. Traditional data compression techniques like Principal Component Analysis (PCA) are popular in remote sensing applications. However, its implementation on MS data may make the data unusable for computer vision (CV) tasks such as image segmentation, especially when applying severe compression. On the other hand, AEs provide great generalization capabilities over complex data, especially when combined with other CV pipelines. For the relevant problem of semantic segmentation, the results are con- siderably degraded when using dimensionality-reduced images with PCA. When using vanilla autoencoders trained with the traditional MSE loss, the segmentation results improve over PCA but are still considerably behind the one obtained with uncompressed data, which indicates a potential domain shift. In this work, we show that training an AE using a combination of the MSE loss and an additional proxy loss based on a pre- trained segmentation module can significantly improve the AE restoration process, alleviating the accuracy drop of semantic segmentation even for strong compression rates. Our code is available at https://github.com/elitonfilho/pca. |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/49L85TH |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/49L85TH |
Language | en |
Target File | 77_nocopyright.pdf |
User Group | crjung@inf.ufrgs.br |
Visibility | shown |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session sponsor subject tertiarymark type url versiontype volume |
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7. Description control | |
e-Mail (login) | crjung@inf.ufrgs.br |
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