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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JMKD35
Repositorysid.inpe.br/sibgrapi/2015/06.19.01.44
Last Update2015:06.19.01.44.22 (UTC) keillernogueira@gmail.com
Metadatasid.inpe.br/sibgrapi/2015/06.19.01.44.22
Metadata Last Update2020:02.19.02.14.03 (UTC) administrator
Citation KeyNogueiraMiraSant:2015:ImSpFe
TitleImproving Spatial Feature Representation from Aerial Scenes by Using Convolutional Networks
FormatOn-line
Year2015
Access Date2021, Dec. 01
Number of Files1
Size5595 KiB
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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
DateAug. 26-29, 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-19 01:44:22 :: keillernogueira@gmail.com -> administrator ::
2020-02-19 02:14:03 :: administrator -> :: 2015
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
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.
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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