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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JMKD35
Repositorysid.inpe.br/sibgrapi/2015/06.19.01.44
Last Update2015:06.19.01.44.22 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.19.01.44.22
Metadata Last Update2022:06.14.00.08.04 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.39
Citation KeyNogueiraMiraSant:2015:ImSpFe
TitleImproving Spatial Feature Representation from Aerial Scenes by Using Convolutional Networks
FormatOn-line
Year2015
Access Date2024, Apr. 30
Number of Files1
Size5595 KiB
2. Context
Author1 Nogueira, Keiller
2 Miranda, Waner O.
3 Santos, Jefersson A. dos
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorPapa, Joćo Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addresskeillernogueira@gmail.com
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador, BA, Brazil
Date26-29 Aug. 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-19 01:44:22 :: keillernogueira@gmail.com -> administrator ::
2022-06-14 00:08:04 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDeep Learning
Remote Sensing
Feature Learning
Image Classification
Machine Learning
High-resolution Images
AbstractThe performance of image classification is highly dependent on the quality of extracted features. Concerning high resolution remote image images, encoding the spatial features in an efficient and robust fashion is the key to generating discriminatory models to classify them. Even though many visual descriptors have been proposed or successfully used to encode spatial features of remote sensing images, some applications, using this sort of images, demand more specific description techniques. Deep Learning, an emergent machine learning approach based on neural networks, is capable of learning specific features and classifiers at the same time and adjust at each step, in real time, to better fit the need of each problem. For several task, such image classification, it has achieved very good results, mainly boosted by the feature learning performed which allows the method to extract specific and adaptable visual features depending on the data. In this paper, we propose a novel network capable of learning specific spatial features from remote sensing images, with any pre-processing step or descriptor evaluation, and classify them. Specifically, automatic feature learning task aims at discovering hierarchical structures from the raw data, leading to a more representative information. This task not only poses interesting challenges for existing vision and recognition algorithms, but also brings huge opportunities for urban planning, crop and forest management and climate modelling. The propose convolutional neural network has six layers: three convolutional, two fully-connected and one classifier layer. So, the five first layers are responsible to extract visual features while the last one is responsible to classify the images. We conducted a systematic evaluation of the proposed method using two datasets: (i) the popular aerial image dataset UCMerced Land-use and, (ii) a multispectral high-resolution scenes of the Brazilian Coffee Scenes. The experiments show that the proposed method outperforms state-of-the-art algorithms in terms of overall accuracy.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2015 > Improving Spatial Feature...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Improving Spatial Feature...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JMKD35
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JMKD35
Languageen
Target Filesibgrapi2015.pdf
User Groupkeillernogueira@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2015/08.03.22.49 8
sid.inpe.br/banon/2001/03.30.15.38.24 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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