@InProceedings{CastroFeiRosDiaSan:2017:CoAnDe,
author = "Castro, Jose Bermudez and Feitosa, Raul Queiroz and Rosa, Laura
Cue La and Diaz, Pedro Achanccaray and Sanches, Ieda",
affiliation = "{Pontifical Catholic University of Rio de Janeiro} and {Pontifical
Catholic University of Rio de Janeiro} and {Pontifical Catholic
University of Rio de Janeiro} and {Pontifical Catholic University
of Rio de Janeiro} and {National Institute for Space Research}",
title = "A Comparative Analysis of Deep Learning Techniques for
Sub-tropical Crop Types Recognition from Multitemporal Optical/SAR
Image Sequences",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Crop Recognition, Multitemporal Images, Autoencoders,
Convolutional Neural Networks.",
abstract = "Remote Sensing (RS) data have been increasingly applied to assess
agricultural yield, production and crop condition. In tropical
areas, crop dynamics are complex due to multiple agricultural
practices such as irrigation, non-tillage, crop rotation and
multiple harvest per year. Spatial and temporal information can
improve the performance in land-cover and crop type classification
tasks. In this context Deep Learning (DL) have emerged as a
powerful state-of-the-art technique in the RS community. This work
presents a comparative analysis of traditional and DL (supervised
and unsupervised) approaches for crop classification on sequences
of multitemporal optical and SAR images. Three different
approaches are compared: the image stacking approach, which is
used as baseline, and two DL based approaches using Autoencoders
(AEs) and Convolutional Neural Networks (CNNs). Experiments were
carried out in two datasets from two different municipalities in
Brazil, Ipu\~{a} in S\~{a}o Paulo state and Campo Verde in Mato
Grosso state. It is shown that CNN and AE outperformed the
traditional approach based on image stacking in terms of Overall
Accuracy and Class Accuracy.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.57",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.57",
language = "en",
ibi = "8JMKD3MGPAW/3PFRT45",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFRT45",
targetfile = "2017_SIBGRAPI_BERMUDEZ.pdf",
urlaccessdate = "2024, Apr. 30"
}