Aircraft Detection by Deep Convolutional Neural Networks

Abstract

Features play crucial role in the performance of classifier for object detection from high-resolution remote sensing images. In this paper, we implemented two types of deep learning methods, deep convolutional neural network (DNN) and deep belief net (DBN), comparing their performances with that of the traditional methods (handcrafted features with a shallow classifier) in the task of aircraft detection. These methods learn robust features from a large set of training samples to obtain a better performance. The depth of their layers (>6 layers) grants them the ability to extract stable and large-scale features from the image. Our experiments show both deep learning methods reduce at least 40% of the false alarm rate of the traditional methods (HOG, LBP+SVM), and DNN performs a little better than DBN. We also fed some multi-preprocessed images simultaneously to one DNN model, and found that such a practice helps to improve the performance of the model obviously with no extra-computing burden adding.