Deep learning para identificação precisa de desmatamentos através do uso de imagens satelitárias de alta resolução
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Data
2019-09-24
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Universidade Federal de Goiás
Resumo
One of the most remarkable advances in Remote Sensing is the devise of the CubeSat
satellite building standard. This technology opens up a myriad of possible applications
that benefit from the higher spatiotemporal resolutions provided by standard-compatible
nanosatellite constellations. In this scenario, one need to investigate the new challenges
and how to address them to take advantage of this new type of Remote Sensing Big
Data. Among these challenges is the development of means to extract useful information
from pixel observations over time in a fine-grained manner. This paper is a seminal
study on the use of a special Deep Learning approach, Recurrent Neural Networks,
to classify long time series of land cover observations. The method was tested against
the problem of identifying areas of deforestation that occurred in a contiguous Cerrado
region (17,810 km2 ) over 13 months using high resolution images from PlanetScope, a
constellation of CubeSat nanosatellites. In addition to temporal analysis, a solution was
needed to make mapping more spatially coherent, which was achieved through the use
of a Convolutional Neural Network architecture known as U-Net, in order to perform the
semantic segmentation of the temporal analysis result performed in the previous step.
The accuracy analysis of the model obtained an F1-score index of 0.9 in identifying
deforestation areas of the region of interest over the analyzed period. Given the high
performance requirements demanded by the volume of data that this new reality imposes
on us, the computational power of parallel processing of a cluster of low cost computers
has been explored, enabling the mapping of the studied region to be accelerated up to
six times. A discussion of limitations and capabilities of the proposed approach is also
presented.
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Aprendizado profundo , LSTM , UNET , Sensoriamento remoto , Desmatamento , Paralelismo , Deep learning , LSTM , UNET , Remote sensing , Deforestation , Parallelism
Citação
TAQUARY, Evandro Carrijo. Deep learning para identificação precisa de desmatamentos através do uso de imagens satelitárias de alta resolução. 2019. 64 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2019.