# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
###### Abstract
Authors of [26] assessed a fully-trained CNN in comparison with a fine-tuned one, to discover utility in the context of aerial scene data. The approach presented in [25] employed the fine-tuning technique to classify hyperspectral images. Authors of [27] suggested to fine-tune the weights of the convolutional layers of the pre-trained CNN to extract better image features. The experimental results presented in [9; 10] showed that fine tuning CNNs that are pre-trained on ImageNet gives good classification accuracy on aerial scene datasets. In order to assess different techniques that exploit deep neural networks, authors of [28] evaluated the best scheme and training method, both for supervised and unsupervised networks. The study [29] tried to determine the optimal way to train neural networks, including greedy layer-wise and unsupervised training. In this paper, we evaluate four different CNN architectures to solve the problem of high-resolution aerial scene classification. We adopt CNNs that are pre-trained on the ImageNet dataset with the purpose of determining their effectiveness in remote sensing image classification tasks. First, we explore the fine-tuning of the weights on the aerial image dataset. In the process of fine-tuning, we remove the final layers of each of the pre-trained networks after the average pooling layer (so called \"network surgery\") and construct a new network head. The new network head consists of: a fully connected layer, dropout, and a softmax layer. Network training is performed on the modified deep neural network. Subsequently, we exploit fine-tuned CNNs for feature extraction and utilize the extracted features for the training of SVM classifiers, which have been successfully applied in other image classification and transfer learning problems [20; 24; 30]. In this paper, SVMs are implemented in two versions: with linear kernel and with Radial Basis Function (RBF) kernel. We use a linear decay learning rate schedule and cyclical learning rates and evaluate their suitability for fine-tuning of pre-trained CNNs for remote sensing image classification. Moreover, we apply label smoothing [31] as a regularization technique and assess its impact on the classification accuracy compared with state-of-the-art methods. Figure 1 shows a flowchart of the proposed method. The main contributions of this paper are (1) evaluation of modern CNNs models on two remote sensing image datasets, (2) analysis of the impact of linear learning rate decay schedule and cyclical learning rates from the aspect of classification accuracy, (3) evaluation of label smoothing on model generalization compared to state-of-the-art techniques, and (4) assessment of the transferability of the features obtained from fine-tuned CNNs and their classification with linear and RBF SVMs classifiers. To the best of our knowledge, the combination of adaptive learning rate and label smoothing was never studied before in the context of aerial scene classification. The remainder of this article is organized, as follows. In Section 2, the methodologies used for fine-tuning of CNNs are presented, and it is described how they were empirically evaluated. The experimental results obtained from the examined remote sensing image classification method are presented in Section 3. Discussion of our method results is given in Section 4. A summary of the results and conclusion of the paper, as well as directions for future research are presented in Section 5.