@InProceedings{CastroFeiRosDiaSan:2017:CoAnDe,
author = "Castro, Jose Bermudez and Feitosa, Raul Queiroz and Rosa, Laura
Cue La and Diaz, Pedro Achanccaray and Sanches, Ieda Del Arco",
affiliation = "{Pontificia Universidade Cat{\'o}lica do Rio de Janeiro
(PUC-Rio)} and {Pontificia Universidade Cat{\'o}lica do Rio de
Janeiro (PUC-Rio)} and {Pontificia Universidade Cat{\'o}lica do
Rio de Janeiro (PUC-Rio)} and {Pontificia Universidade
Cat{\'o}lica do Rio de Janeiro (PUC-Rio)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)}",
title = "A comparative analysis of deep learning techniques for
sub-tropical crop types recognition from multitemporal optical/SAR
image sequences",
booktitle = "Proceedings...",
year = "2017",
pages = "17320353",
organization = "SIBGRAPI Conference on Graphics, Patterns and Images, 30.",
note = "Este registro {\'e} um duplicado de um registro oficial do
SIBGRAPI e, como tal, deve ser mantido escondido, mas n{\~a}o
pode ser removido para evitar eventual quebra de v{\'{\i}}nculo
(Banon).",
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, Ipua\̃ in Sa\̃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 = "Niteroi, Brazil",
conference-year = "17-20 Oct.",
language = "en",
targetfile = "castro_comparative.pdf",
urlaccessdate = "27 abr. 2024"
}