Eficiência de Redes Neurais Artificiais na Classificação de Uso e do Solo da Bacia Hidrográfica do Rio Japaratuba - SE

Barros, Glauber Vinícius Pinto de; Gomes, Heliofábio Barros; Santos, Felipe Souza dos; Cruz, M. A. S.; Nascimento, Paulo Sérgio de Rezende; Costa, Rafaela Lisboa; Júnior, Rodrigo Lins da Rocha; Silva, Fabrício Daniel dos Santos

doi:10.1590/0102-7786355000009

Cited by 1 publication

(1 citation statement)

References 10 publications

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“…ANN models have shown superior performance for supervised classifications [13] and are often used in remote sensing due to their ease of learning complex class patterns [31]. Studies such as Gava et al [13] corroborates our findings in relation to classification analyses using ANN.…”

Section: Discussionsupporting

confidence: 85%

Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning

Pereira Ribeiro Teodoro,

Estevão,

Santana

et al. 2023

Forests

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The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories to be completed efficiently, reducing labor and time. This is the first study to evaluate the effectiveness of classification of five eucalyptus species (E. camaldulensis, Corymbia citriodora, E. saligna, E. grandis, and E. urophyla) using hyperspectral images and machine learning. Spectral readings were taken from 200 leaves of each species and divided into three dataset sizes: one set containing 50 samples per species, a second with 100 samples per species, and a third set with 200 samples per species. The ML algorithms tested were multilayer perceptron artificial neural network (ANN), decision trees (J48 and REPTree algorithms), and random forest (RF). As a control, a conventional approach by logistic regression (LR) was used. Eucalyptus species were classified by ML algorithms using a randomized stratified cross-validation with 10 folds. After obtaining the percentage of correct classification (CC) and F-measure accuracy metrics, the means were grouped by the Scott–Knott test at 5% probability. Our findings revealed the existence of distinct spectral curves between the species, with the differences being more marked from the 700 nm range onwards. The most accurate ML algorithm for identifying eucalyptus species was ANN. There was no statistical difference for CC between the three dataset sizes. Therefore, it was determined that 50 leaves would be sufficient to accurately differentiate the eucalyptus species evaluated. Our study represents an important scientific advance for forest inventories and breeding programs with applications in both forest plantations and native forest areas as it proposes a fast, accurate, and large-scale species-level classification approach.

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Section: Discussionsupporting

confidence: 85%