Optical time series for the separation of land cover types with similar spectral signatures: cocoa agroforest and forest
Created by W.Langdon from
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- @Article{Batista:2022:IJRS,
-
author = "Joao E. Batista and Nuno M. Rodrigues and
Ana I. R. Cabral and Maria J. P. Vasconcelos and
Adriano Venturieri and Luiz G. T. Silva and Sara Silva",
-
title = "Optical time series for the separation of land cover
types with similar spectral signatures: cocoa
agroforest and forest",
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journal = "International Journal of Remote Sensing",
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year = "2022",
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volume = "43",
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number = "9",
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pages = "3298--3319",
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keywords = "genetic algorithms, genetic programming, Cocoa
agroforest classification, land cover mapping, machine
learning, time series, tropical areas",
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publisher = "Taylor \& Francis",
-
DOI = "doi:10.1080/01431161.2022.2089540",
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data_url = "https://github.com/jespb/Cocoa_PublicDS",
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size = "22 pages",
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abstract = "One of the main applications of machine learning (ML)
in remote sensing (RS) is the pixel-level
classification of satellite images into land cover
types. Although classes with different spectral
signatures can be easily separated, e.g. aquatic and
terrestrial land cover types, others have similar
spectral signatures and are hard to separate using only
the information within a single pixel. This work
focused on the separation of two cover types with
similar spectral signatures, cocoa agroforest and
forest, over an area in Para, Brazil. For this, we
study the training and application of several ML
algorithms on datasets obtained from a single composite
image, a time-series (TS) composite obtained from the
same location and by preprocessing the TS composite
using simple TS preprocessing techniques. As expected,
when ML algorithms are applied to a dataset obtained
from a composite image, the median producer accuracy
(PA) and user accuracy (UA) in those two classes are
significantly lower than the median overall accuracy
(OA) for all classes. The second dataset allows the ML
models to learn the evolution of the spectral
signatures over 5 months. Compared to the first
dataset, the results indicate that ML models generalise
better using TS data, even if the series are short and
without any preprocessing. This generalization is
further improved in the last dataset. The ML models are
subsequently applied to an area with different
geographical bounds. These last results indicate that,
out of seven classifiers, the popular random forest
(RF) algorithm ranked fourth, while XGBoost (xGB)
obtained the best results. The best OA, as well as the
best PA/UA balance, were obtained by performing feature
construction using the M3GP algorithm and then applying
XGB to the new extended dataset.",
-
notes = "a LASIGE, Faculty of Sciences, University of Lisbon,
Portugal",
- }
Genetic Programming entries for
Joao E Batista
Nuno Miguel Rodrigues Domingos
Ana Isabel Rosa Cabral
Maria Jose Vasconcelos
Adriano Venturieri
Luiz Guilherme Teixeira Silva
Sara Silva
Citations