Pre-trained VGGNet Architecture for Remote-Sensing Image Scene Classification

Muhammad, Usman; Wang, Weiqiang; Chattha, Shahbaz Pervaiz; Ali, Sajid

doi:10.1109/icpr.2018.8545591

Cited by 68 publications

(35 citation statements)

References 17 publications

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“…This CNN architecture is made up of three connected layers (the FC6, FC7, and FC8layer) in addition to thirteen convolutional layers [26]. Having a combination of two 3x3 convolutional layers creates a 5x5 receptive field that has a number of kernels or learnable filters whereby each layer unit receives input from units located in the previous layer.…”

Section: Vggnet (Geometry Group Network)mentioning

confidence: 99%

“…Executing these integrated layers requires a rectified linear units (ReLU), which has an average or max-pooling operation, which is critical for multi-layer networks [27]. The representation's spatial size is decreased by pooling layers while ReLU, which in this case is the half-wave rectifier function f(x)=max(x,0), accelerates the training phase and prohibits case of overfitting [26]. The final output layer is made up of the fully-connected layers, whereby a neuron from one layer is connected to numbers in the previous volume.…”

Section: Vggnet (Geometry Group Network)mentioning

confidence: 99%

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Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

Aquil

Ishak

2021

IJ-AI

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<span id="docs-internal-guid-01580d49-7fff-6f2a-70d1-7893ec0a6e14"><span>Plant diseases are a major cause of destruction and death of most plants and especially trees. However, with the help of early detection, this issue can be solved and treated appropriately. A timely and accurate diagnosis is critical in maintaining the quality of crops. Recent innovations in the field of deep learning (DL), especially in convolutional neural networks (CNNs) have achieved great breakthroughs across different applications such as the classification of plant diseases. This study aims to evaluate scratch and pre-trained CNNs in the classification of tomato plant diseases by comparing some of the state-of-the-art architectures including densely connected convolutional network (Densenet) 120, residual network (ResNet) 101, ResNet 50, ReseNet 30, ResNet 18, squeezenet and Vgg.net. The comparison was then evaluated using a multiclass statistical analysis based on the F-Score, specificity, sensitivity, precision, and accuracy. The dataset used for the experiments was drawn from 9 classes of tomato diseases and a healthy class from PlantVillage. The findings show that the pretrained Densenet-120 performed excellently with 99.68% precision, 99.84% F-1 score, and 99.81% accuracy, which is higher compared to its non-trained based model showing the effectiveness of using a combination of a CNN model with fine-tuning adjustment in classifying crop diseases.</span></span>

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Section: Vggnet (Geometry Group Network)mentioning

confidence: 99%

Section: Vggnet (Geometry Group Network)mentioning

confidence: 99%

Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

Aquil

Ishak

2021

IJ-AI

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“…Deep learning can learn high-level features in data by using structures composed of multiple non-linear transformations. In view of this, we test our model on deep features, which are trained from two kinds of deep learning-based CNN features: AlexNet [46] and VGGNet [47] for its superiority performance in feature learning and classification. The details of these models are tabulated in Table 8.…”

Section: Nwpu-resisc45 Datasetmentioning

confidence: 99%

Coupled Least Squares Support Vector Ensemble Machines

Wornyo

Shen

2019

Information

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The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications. In this paper, we propose a novel coupled least squares support vector ensemble machine (C-LSSVEM). The proposed coupling ensemble helps improve robustness and produce good classification performance than the single model approach. The proposed C-LSSVEM can choose appropriate kernel types and their parameters in a good coupling strategy with a set of classifiers being trained simultaneously. The proposed method can further minimize the total loss of ensembles in kernel space. Thus, we form an ensemble regressor by co-optimizing and weighing base regressors. Experiments conducted on several datasets such as artificial datasets, UCI classification datasets, UCI regression datasets, handwritten digits datasets and NWPU-RESISC45 datasets, indicate that C-LSSVEM performs better in achieving the minimal regression loss and the best classification accuracy relative to selected state-of-the-art regression and classification techniques.

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“…The final classification result was obtained by averaging all model outputs. A four-layer feature representation was constructed with two convolutional layers and two full connected layers of a VGGNet model [41]. Then, canonical correlation analysis (CCA) was used for feature fusion.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Scene Classification Based on Fusion Method

et al. 2021

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Remote sensing image scene classification is a hot research area for its wide applications. More recently, fusion-based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion-based method for remote sensing image scene classification from another viewpoint. First, it is categorized as front side fusion mode, middle side fusion mode, and back side fusion mode. For each fusion mode, the related methods are introduced and described. Then, classification performances of the single side fusion mode and hybrid side fusion mode (combinations of single side fusion) are evaluated. Comprehensive experiments on UC Merced, WHU-RS19, and NWPU-RESISC45 datasets give the comparison result among various fusion methods. The performance comparisons of various modes, and interactions among different fusion modes are also discussed. It is concluded that (1) fusion is an effective way to improve model performance, (2) back side fusion is the most powerful fusion mode, and (3) method with random crop+multiple backbone+average achieves the best performance.

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Pre-trained VGGNet Architecture for Remote-Sensing Image Scene Classification

Cited by 68 publications

References 17 publications

Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

Coupled Least Squares Support Vector Ensemble Machines

Remote Sensing Image Scene Classification Based on Fusion Method

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