Simple Yet Effective Fine-Tuning of Deep CNNs Using an Auxiliary Classification Loss for Remote Sensing Scene Classification

Bazi, Yakoub; Rahhal, Mohamad Mahmoud Al; Alhichri, Haikel; Alajlan, Naif

doi:10.3390/rs11242908

Cited by 76 publications

(39 citation statements)

References 37 publications

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“…In the Faster R-CNN, different feature extraction networks lead to different performances of the final object detection [34]. VGGNet and ResNet performed well in classification of the remote sensing images [35,36]. Therefore, VGGNet and ResNet were used as the feature extraction networks in this study.…”

Section: Comparisons Between Different Feature Extraction Networkmentioning

confidence: 99%

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

et al. 2020

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Maize tassels play a critical role in plant growth and yield. Extensive RGB images obtained using unmanned aerial vehicle (UAV) and the prevalence of deep learning provide a chance to improve the accuracy of detecting maize tassels. We used images from UAV, a mobile phone, and the Maize Tassel Counting dataset (MTC) to test the performance of faster region-based convolutional neural network (Faster R-CNN) with residual neural network (ResNet) and a visual geometry group neural network (VGGNet). The results showed that the ResNet, as the feature extraction network, was better than the VGGNet for detecting maize tassels from UAV images with 600 × 600 resolution. The prediction accuracy ranged from 87.94% to 94.99%. However, the prediction accuracy was less than 87.27% from the UAV images with 5280 × 2970 resolution. We modified the anchor size to [852, 1282, 2562] in the region proposal network according to the width and height of pixel distribution to improve detection accuracy up to 89.96%. The accuracy reached up to 95.95% for mobile phone images. Then, we compared our trained model with TasselNet without training their datasets. The average difference of tassel number was 1.4 between the calculations with 40 images for the two methods. In the future, we could further improve the performance of the models by enlarging datasets and calculating other tassel traits such as the length, width, diameter, perimeter, and the branch number of the maize tassels.

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Section: Comparisons Between Different Feature Extraction Networkmentioning

confidence: 99%

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

et al. 2020

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“…From Table 6, it can be concluded that there are methods that outperform our proposed method. One of them uses fine-tuning of EfficientNet-B3 with auxiliary classifier [4]. EfficientNet-B3 yields better top-1 and top-5 classification accuracy on ImageNet data set compared to the pre-trained CNNs utilized in this article, and this is probably the main reason for the better overall accuracy.…”

Section: Classification Of the Nwpu-resisc45 Data Setmentioning

confidence: 99%

“…The latest techniques that include deep learning methods based on Convolutional Neural Networks (CNNs) have shown remarkable improvement in classification accuracy as compared to older ones based on handcrafted features [2,3]. The effectiveness of solutions based on CNNs lies in the possibility to perform knowledge transfer from pre-trained CNNs [4]. The knowledge transfer for image classification can be conducted in different ways, including feature extraction and fine-tuning [5,6].…”

Section: Introductionmentioning

confidence: 99%

Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing

Petrovska¹,

Atanasova-Pacemska

Corizzo

et al. 2020

Applied Sciences

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Remote Sensing (RS) image classification has recently attracted great attention for its application in different tasks, including environmental monitoring, battlefield surveillance, and geospatial object detection. The best practices for these tasks often involve transfer learning from pre-trained Convolutional Neural Networks (CNNs). A common approach in the literature is employing CNNs for feature extraction, and subsequently train classifiers exploiting such features. In this paper, we propose the adoption of transfer learning by fine-tuning pre-trained CNNs for end-to-end aerial image classification. Our approach performs feature extraction from the fine-tuned neural networks and remote sensing image classification with a Support Vector Machine (SVM) model with linear and Radial Basis Function (RBF) kernels. To tune the learning rate hyperparameter, we employ a linear decay learning rate scheduler as well as cyclical learning rates. Moreover, in order to mitigate the overfitting problem of pre-trained models, we apply label smoothing regularization. For the fine-tuning and feature extraction process, we adopt the Inception-v3 and Xception inception-based CNNs, as well the residual-based networks ResNet50 and DenseNet121. We present extensive experiments on two real-world remote sensing image datasets: AID and NWPU-RESISC45. The results show that the proposed method exhibits classification accuracy of up to 98%, outperforming other state-of-the-art methods.

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“…Yu et al [29] proposed the attention GAN, which integrates GANs with the attention mechanism to enhance the representation power of the discriminator for aerial scene classification. The authors in [30] introduced a simple fine-tuning method using an auxiliary classification loss. They showed how to combat the vanishing gradient problem using an auxiliary loss function.…”

Section: Introductionmentioning

confidence: 99%

Vision Transformers for Remote Sensing Image Classification

et al. 2021

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In this paper, we propose a remote-sensing scene-classification method based on vision transformers. These types of networks, which are now recognized as state-of-the-art models in natural language processing, do not rely on convolution layers as in standard convolutional neural networks (CNNs). Instead, they use multihead attention mechanisms as the main building block to derive long-range contextual relation between pixels in images. In a first step, the images under analysis are divided into patches, then converted to sequence by flattening and embedding. To keep information about the position, embedding position is added to these patches. Then, the resulting sequence is fed to several multihead attention layers for generating the final representation. At the classification stage, the first token sequence is fed to a softmax classification layer. To boost the classification performance, we explore several data augmentation strategies to generate additional data for training. Moreover, we show experimentally that we can compress the network by pruning half of the layers while keeping competing classification accuracies. Experimental results conducted on different remote-sensing image datasets demonstrate the promising capability of the model compared to state-of-the-art methods. Specifically, Vision Transformer obtains an average classification accuracy of 98.49%, 95.86%, 95.56% and 93.83% on Merced, AID, Optimal31 and NWPU datasets, respectively. While the compressed version obtained by removing half of the multihead attention layers yields 97.90%, 94.27%, 95.30% and 93.05%, respectively.

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Simple Yet Effective Fine-Tuning of Deep CNNs Using an Auxiliary Classification Loss for Remote Sensing Scene Classification

Cited by 76 publications

References 37 publications

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing

Vision Transformers for Remote Sensing Image Classification

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