# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
###### Abstract
The most popular approach for feature extraction using CNNs is to employ the extracted features from the upper convolutional layers, or the last fully connected layer that precedes the classification layers. However, when the target task of interest significantly differs from the original task, features extracted from lower convolutional layers appear to be more suitable [6]. The most widely used CNN models for aerial scene classification are CaffeNet, GoogleNet, and VGGNet [12; 10; 13; 14; 15]. These neural networks consist of approximately 30 layers and present a huge number of parameters. The study conducted in [16] evaluated deep features for the classification of traditional images, whether alone or combined with other features. Authors of [9] utilized extracted features from two pre-trained CNNs and, in that way, performed classification of high-resolution aerial scene images. They proposed features that were obtained by fusion of the activations from the mid-level layers and the last fully connected layers of the CNN schemes. Before feature fusion is performed, feature coding algorithms are applied to activations from convolutional layers. VGGNet is used for extracting features from different network layers, and then features are transformed by Discriminant Correlation Analysis (DCA) [13]. The transformed features are concatenated and, after that, a SVM classifier is applied for image classification [17]. The rationale of this process is to use convolution as an efficient way to extract a new compact and effective feature representation from raw data, simplifying the subsequent classification task. This capability of neural networks has also been fruitfully exploited in order to extract feature vector representations for predictive tasks also in the context of graph data [18; 19] and time series data [20; 21]. Feature fusion can also be found in other articles [22; 23]. Two schemes are proposed in the literature. The former uses the original network for feature extraction from RGB images, while the mapped Local Binary Pattern (LBP) coded network is used for feature extraction from LBP feature maps. After this step, feature fusion is performed by the concatenation layer: features go through fully connected layers and they are classified at the end. The latter uses a saliency coded network instead of a mapped LBP coded network. The study [14] used Recurrent Neural Networks (RNNs) for remote sensing image classification. RNNs are employed to build the attention mechanism. In [12] is presented a new loss function, with enforcing metric learning to CNNs features. A metric learning loss was combined with a standard optimization loss (cross-entropy loss). This approach resulted in features that belong to images from the same image class to be very close, while features extracted from images from different classes to be very distant. The approach presented in [24] extracted features from different layers of pre-trained CNNs and concatenated them with prior dimensionality reduction through Principal Component Analysis (PCA). Logistic Regression Classifier (LRC) and SVMs were applied to the compound features. The classification accuracy of a pre-trained CNNs can be further improved through fine-tuning of the weights. Fine-tuning is a transfer learning method that adjusts the parameters of a pre-trained CNN by resuming the training of the network with a new dataset, that possibly addresses a new task with a different number of classes than the initial output layer of the initial CNN architecture. Fine tuning trains the network with small initial learning rate and a reduced number of training epochs, compared to a complete training process from scratch. During this process, the cost function achieves a better minimum compared to a case with random weight initialization. Several articles [25; 26] in the remote sensing community have also studied the advantages of fine-tuning pre-trained CNNs.