@Article{KuckSiSaBiShSi:2021:ChDeSe,
author = "Kuck, Tahisa Neitzel and Silva Filho, Paulo Fernando Ferreira and
Sano, Edson Eyji and Bispo, Popyanna da Concei{\c{c}}{\~a}o and
Shiguemori, Elcio Hideiti and Silva, Ricardo Dal'Agnol da",
affiliation = "{Instituto de Estudos Avan{\c{c}}ados (IEAv)} and {Instituto de
Estudos Avan{\c{c}}ados (IEAv)} and {Universidade de
Bras{\'{\i}}lia (UnB)} and {University of Manchester} and
{Instituto de Estudos Avan{\c{c}}ados (IEAv)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Change detection of selective logging in the brazilian amazon
using x-band sar data and pre-trained convolutional neural
networks",
journal = "Remote Sensing",
year = "2021",
volume = "13",
number = "23",
pages = "e4944",
month = "Dec.",
keywords = "Convolutional neural networks, Selective logging, Synthetic
aperture radar.",
abstract = "It is estimated that, in the Brazilian Amazon, forest degradation
contributes three times more than deforestation for the loss of
gross above-ground biomass. Degradation, in particular those
caused by selective logging, result in features whose detection is
a challenge to remote sensing, due to its size, space
configuration, and geographical distribution. From the available
remote sensing technologies, SAR data allow monitoring even during
adverse atmospheric conditions. The aim of this study was to test
different pre-trained models of Convolutional Neural Networks
(CNNs) for change detection associated with forest degradation in
bitemporal products obtained from a pair of SAR COSMO-SkyMed
images acquired before and after logging in the Jamari National
Forest. This area contains areas of legal and illegal logging, and
to test the influence of the speckle effect on the result of this
classification by applying the classification methodology on
previously filtered and unfiltered images, comparing the results.
A method of cluster detections was also presented, based on
density-based spatial clustering of applications with noise
(DBSCAN), which would make it possible, for example, to guide
inspection actions and allow the calculation of the intensity of
exploitation (IEX). Although the differences between the tested
models were in the order of less than 5%, the tests on the RGB
composition (where R = coefficient of variation; G = minimum
values; and B = gradient) presented a slightly better performance
compared to the others in terms of the number of correct
classifications for selective logging, in particular using the
model Painters (accuracy = 92%) even in the generalization tests,
which presented an overall accuracy of 87%, and in the test on RGB
from the unfiltered image pair (accuracy of 90%). These results
indicate that multitemporal X-band SAR data have the potential for
monitoring selective logging in tropical forests, especially in
combination with CNN techniques.",
doi = "10.3390/rs13234944",
url = "http://dx.doi.org/10.3390/rs13234944",
issn = "2072-4292",
language = "en",
targetfile = "remotesensing-13-04944-v2.pdf",
urlaccessdate = "08 maio 2024"
}