An Approach of Transferring Pre-trained Deep Convolutional Neural Networks for Aerial Scene Classification

Feature selection or feature extraction plays a vital role in image classification task. Since the advent of deep learning methods, significant efforts have been given by researchers to obtain an optimal feature set of images for improving classification performance. Though several deep architectures of Convolutional Neural Networks (CNNs) have been successfully designed but training such deep architectures with small datasets like aerial scenes often leads to overfitting hence affects the classification accuracy. To tackle this issue in past few works, pre-trained CNNs are adopted as feature extractor where features are directly transferred to train only the classification layer for classifying images on the target dataset. In this work, an approach of feature extraction is proposed where both "multi-layer" and "multi-model" features are extracted from pre-trained CNNs. "Multi-layer" features are concatenation of features from multiple layers within a same CNN and "Multi-model" are concatenation of features from different CNN models. The concatenated features are further reduced with some method to obtain an optimal feature set.