# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
###### Abstract  
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. remote sensing; convolutional neural network; fine-tuning; learning rate scheduler; cyclical learning rates; label smoothing; classification accuracy +
Footnote †: journal: Applied  
0000-0001-9991-455X][PERSON] 0000-0001-9992-0886-2221][PERSON] 0000-0001-9992-0886-2217][PERSON] 0000-0001-9992-0886-2217][PERSON] 0000-0001-9992-0886-2217][PERSON] 0000-0001-9992-0886-2217][PERSON] 0000-0001-999-2223-22331][PERSON] 0000-0001-999-2233-2331][PERSON] 0000-0002-0002-0886-2217][PERSON] 0000-0001-999-2233-2331][PERSON] 0000-0001-999-22333-2331][PERSON] 0000-0002-0002-0886-221  
interest in resent research, due to its importance in a wide range of applications, including the surveillance of airports and aviation protection, flora monitoring in agriculture, and recognition of earth cover changes in environmental engineering [1]. RS image classification is possible thanks to the availability of RS images datasets that were collected from earth observation platforms, such as satellites, aerial systems, and unmanned aerial vehicles. The problem is complex and relies on the representation of salient image characteristics by means of high-level features. 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]. There are numerous research studies that show that CNNs trained on one classification problem (such as ImageNet) can be successfully exploited to extract features from images in different tasks [7]. Excellent classification results were also achieved in aerial scene classification [8; 9; 10]. The first case of adoption of pre-trained CNN schemes for remote sensing image classification was performed by [8], where the pre-trained CNNs AlexNet and Overfeat [11] were employed for feature extraction, and the activations from the first fully connected layer of the CNN architectures were used as image representations. Excellent results with two remote sensing datasets are reported in [8], outperforming several handcrafted visual descriptors.