TRAINING DEEP CONVOLUTION NEURAL NETWORK WITH HARD EXAMPLE MINING FOR AIRPORT DETECTION

The geometrical characteristic and low-level manually designed features are usually used to detect airports in optical remote sensing images. But it is insufficient to describe airport in low resolution and illumination environment. This paper presents a hard example mining algorithm to train the end-to-end deep convolutional neural network for airport detection in complex situation. Compared with conventional airport detection methods which design specfic low-level manually designed features for high-resolution remote sensing images, an end-to-end network can mine the general characteristic among the training samples and learn high-level features in multi-scale and multi-view remote sensing images. Meanwhile, an automatic hard example mining principle is introduced to make training more efficiently and accurately. The proposed method is validated on a multi-scale and multi-view dataset collected from Google Earth. The experimental results demonstrate that the proposed method is robust and efficient, and superior to the state-of-the-art airport detection models.