Aiming at the problem of insufficient representation ability of weak and small objects and overlapping detection boxes in airplane object detection, an effective airplane detection method in remote sensing images based on multilayer feature fusion and an improved nonmaximal suppression algorithm is proposed. Firstly, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airplane images using a limited amount of data. Then, the L2 norm normalization, feature connection, scale scaling, and feature dimension reduction are introduced to achieve effective fusion of low- and high-level features. Finally, a nonmaximal suppression method based on a soft decision function is proposed to solve the overlap problem of detection boxes. The experimental results show that the proposed method can effectively improve the representation ability of weak and small objects, as well as quickly and accurately detect airplane objects in the airport area.
Predicting the Lossless Compression Ratio of Remote Sensing Images with Configurational Entropy
