Using a VGG-16 Network for Individual Tree Species Detection with an Object-Based Approach

Rezaee, Mohammad; Zhang, Yun; Mishra, Rakesh; Tong, Fei; Tong, Hengjian

doi:10.1109/prrs.2018.8486395

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…The feature extraction process was implemented when using the LeNet [8], AlexNet, and VGG-16 [9] architectures. In the feature extraction and classification stage, the RMSprop, Adam, and Stochastic Gradient Descent (SGD) [10], [11] optimization methods were used.…”

Section: Data Set and Methodsmentioning

confidence: 99%

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Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Toğaçar¹,

Ergen²,

Sertkaya³

2019

ELEKTRON ELEKTROTECH

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The white blood cells produced in the bone marrow and lymphoid tissue known as leucocytes are an important part of the immune system to protect the body against foreign invaders and infectious disease. These cells, which do not have color, have a few days or several weeks of life. A lot of clinic experience is required for a doctor to detect the amount of white blood cells in human blood and classify it. Thus, early and accurate diagnosis can be made in the formation of various disease types, including infection on the immune system, such as anemia and leukemia, while evaluating and determining the disease of a patient. The white blood cells can be separated into four subclasses, such as Eosinophil, Lymphocyte, Monocyte, and Neutrophil. This study focuses on the separation of the white blood cell images by the classification process using convolutional neural network models, which is a deep learning model. A deep learning network, which is slow in the training step due to the complex architecture, but fast in the test step, is used for the feature extraction instead of intricate methods. For the subclass separation of white blood cells, the experimental results show that the AlexNet architecture gives the correct recognition rate among the convolutional neural network architectures tested in the study. Various classifiers are performed on the features derived from the AlexNet architecture to evaluate the classification performance. The best performance in the classification of white blood cells is given by the quadratic discriminant analysis classifier with the accuracy of 97.78 %.

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Section: Data Set and Methodsmentioning

confidence: 99%

“…The structure of VGG-16 architecture consists of five convolutional layers, a pooling layer, and three fully connected layers. The final layer consists of Softmax layer used in the classification process [9].…”

Section: B Feature Extractionmentioning

confidence: 99%

Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Toğaçar¹,

Ergen²,

Sertkaya³

2019

ELEKTRON ELEKTROTECH

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“…The exception was the Sentinel-FRI model at the stand scale, where RF slightly outperformed CNN. The generally higher performance of CNN models highlights the considerable potential of neural network approaches, which have received support in other studies as well (e.g., [16,23,41]). We did not incorporate vegetation indices into our analysis, which might have improved our RF models (e.g., [16,17]), but might have decreased performance in the CNN models [16].…”

Section: Discussionmentioning

confidence: 68%

“…One was random forest, which is a relatively simple approach that has proven successful in many studies (e.g., [21,22]). However, several authors reported better prediction with multi-layer neural networks (e.g., [16,23]). Hence, as a second method we used convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

Use of Sentinel-2 Data to Improve Multivariate Tree Species Composition in a Forest Resource Inventory

et al. 2021

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Aerial-photo interpreted inventories of forest resources, including tree species composition, are valuable in forest resource management, but are expensive to create and can be relatively inaccurate. Because of differences among tree species in their spectral properties and seasonal phenologies, it might be possible to improve such forest resource inventory information (FRI) by using it in concert with multispectral satellite information from multiple time periods. We used Sentinel-2 information from nine spectral bands and 12 dates within a two-year period to model multivariate percent tree species composition in >51,000 forest stands in the FRI of south-central Ontario, Canada. Accuracy of random forest (RF) and convolutional neural network (CNN) predictions were tested using species-specific basal area information from 155 0.25-ha field plots. Additionally, we created models using the Sentinel-2 information in concert with the field data and compared the accuracy of these models and the FRI-based models by use of basal areas from a second (13.7-ha) field data set. Based on average R2 values across species in the two field data sets, the Sentinel-FRI models outperformed the FRI, showing 1.5- and 1.7-fold improvements relative to the FRI for RF and 2.1- and 2.2-fold improvements for CNN (mean R2: 0.141–0.169 (FRI); 0.217–0.295 (RF); 0.307–0.352 (CNN)). Models created with the field data performed even better: improvements relative to the FRI were 2.1-fold for RF and 2.8-fold for CNN (mean R2: 0.169 (FRI); 0.356 (RF); 0.469 (CNN)). As predicted, R2 values between FRI- and field-trained predictions were higher than R2 values with the FRI. Of the 21 tree species evaluated, 8 relatively rare species had poor models in all cases. Our multivariate approach allowed us to use more FRI stands in model creation than if we had been restricted to stands dominated by single species and allowed us to map species abundances at higher resolution. It might be possible to improve models further by use of tree stem maps and incorporation of the effects of canopy disturbances.

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“…Different tree species were considered in different studies, and the spectral differences between the tree species were also one of the factors influencing the classification accuracy. Rezaee et al [67] utilized VGG-16 to classify four tree species, including red maple, birch, spruce, and pine. The two deciduous broad-leaved species, red maple and birch, yielded higher accuracies than the two evergreen conifers species, spruce and pine.…”

Section: Discussionmentioning

confidence: 99%

Individual Tree Species Classification Based on Convolutional Neural Networks and Multitemporal High-Resolution Remote Sensing Images

Guo

Jing

et al. 2022

Sensors

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The classification of individual tree species (ITS) is beneficial to forest management and protection. Previous studies in ITS classification that are primarily based on airborne LiDAR and aerial photographs have achieved the highest classification accuracies. However, because of the complex and high cost of data acquisition, it is difficult to apply ITS classification in the classification of large-area forests. High-resolution, satellite remote sensing data have abundant sources and significant application potential in ITS classification. Based on Worldview-3 and Google Earth images, convolutional neural network (CNN) models were employed to improve the classification accuracy of ITS by fully utilizing the feature information contained in different seasonal images. Among the three CNN models, DenseNet yielded better performances than ResNet and GoogLeNet. It offered an OA of 75.1% for seven tree species using only the WorldView-3 image and an OA of 78.1% using the combinations of WorldView-3 and autumn Google Earth images. The results indicated that Google Earth images with suitable temporal detail could be employed as auxiliary data to improve the classification accuracy.

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Using a VGG-16 Network for Individual Tree Species Detection with an Object-Based Approach

Cited by 20 publications

References 17 publications

Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Use of Sentinel-2 Data to Improve Multivariate Tree Species Composition in a Forest Resource Inventory

Individual Tree Species Classification Based on Convolutional Neural Networks and Multitemporal High-Resolution Remote Sensing Images

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