Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data

Sothe, Camile; Almeida, Cláudia Maria de; Schimalski, Marcos Benedito; Rosa, Laura Elena Cué La; Castro, Jose David Bermudez; Feitosa, Raul Queiroz; Dalponte, Michele; Lima, Carla Luciane; Liesenberg, Veraldo; Miyoshi, Gabriela Takahashi; Tommaselli, Antônio Maria Garcia

doi:10.1080/15481603.2020.1712102

Cited by 123 publications

(60 citation statements)

References 93 publications

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“…Similar to our study, Hartling et al (2019) emphasized the ability of the CNN to automatically extract information from the input dataset, given that the addition of hand-engineered features does not always result in higher accuracies. For instance, the studies conducted by Sothe et al (2019) and Maschler et al (2018) observed that the inclusion of texture information derived from GLCM did not significantly increase the accuracy of tree species classification when using SVM or RF methods. Zhao and Du (2016) noticed that the method involving spatial features extracted from a CNN produced maps with less "salt and pepper" effect, which was also observed in this study ( Figure 6).…”

Section: Resultsmentioning

confidence: 99%

“…UAV-borne sensors enable to collect data even under cloud cover conditions. Moreover, they are flexible regarding spatial and temporal resolution, what makes them a cost-effective and operational solution for tree species classification (Nevalainen et al, 2017;Tuominen et al, 2018;Sothe et al, 2019;Miyoshi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…To deal with this, many studies resort to textural features as a way to include spatial information in the classification process. The gray-level co-occurrence matrix (GLCM), for instance, is frequently applied for tree species classification (Franklin, Ahmed, 2017;Maschler et al, 2018;Ferreira et al, 2019;Sothe et al, 2019). However, such techniques commonly require predefined spatial filter and other parameters which are subjectively determined by the user according to his/her knowledge of the problem.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning methods are able to automatically extract high-level spatial features from hyperspectral data (Chen et al, 2016;Zhao, Du, 2016;Signoroni et al, 2019), showing great robustness and effectiveness in image classification (Chen et al, 2014;Li et al, 2017;Wagner et al, 2019). Among these methods, the convolutional neural network (CNN) algorithm is a supervised deep learning model that has been producing promising results in tree species classification (Pölönen et al, 2018;Fricker et al 2019;Hartling et al, 2019;Sothe et al, 2019). However, few studies explored the CNN for tree species classification in (sub)tropical forests (e.g., Sothe et al, 2019) and the potential of deep features extracted from a CNN for this purpose remains unknown.…”

Section: Introductionmentioning

confidence: 99%

“…Among these methods, the convolutional neural network (CNN) algorithm is a supervised deep learning model that has been producing promising results in tree species classification (Pölönen et al, 2018;Fricker et al 2019;Hartling et al, 2019;Sothe et al, 2019). However, few studies explored the CNN for tree species classification in (sub)tropical forests (e.g., Sothe et al, 2019) and the potential of deep features extracted from a CNN for this purpose remains unknown.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating a Convolutional Neural Network for Feature Extraction and Tree Species Classification Using Uav-Hyperspectral Images

Sothe

Rosa

Almeida

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced feature extraction and classification methods. Different from the traditional feature extraction methods, that highly depend on user’s knowledge, the convolutional neural network (CNN)-based method can automatically learn and extract the spatial-related features layer by layer. However, in order to capture significant features of the data, the CNN classifier requires a large number of training samples, which are hardly available when dealing with tree species in tropical forests. This study investigated the following topics concerning the classification of 14 tree species in a subtropical forest area of Southern Brazil: i) the performance of the CNN method associated with a previous step to increase and balance the sample set (data augmentation) for tree species classification as compared to the conventional machine learning methods support vector machine (SVM) and random forest (RF) using the original training data; ii) the performance of the SVM and RF classifiers when associated with a data augmentation step and spatial features extracted from a CNN. Results showed that the CNN classifier outperformed the conventional SVM and RF classifiers, reaching an overall accuracy (OA) of 84.37% and Kappa of 0.82. The SVM and RF had a poor accuracy with the original spectral bands (OA 62.67% and 59.24%) but presented an increase between 14% and 21% in OA when associated with a data augmentation and spatial features extracted from a CNN.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%