Hao Sun

TL;DR: This paper proposes a unified and effective method for simultaneously detecting multi-class objects in remote sensing images with large scales variability, and shows that the method is more accurate than existing algorithms and is effective for multi-modalRemote sensing images.

TL;DR: To accurately extract vehicle-like targets, an accurate-vehicle-proposal-network (AVPN) based on hyper feature map which combines hierarchical feature maps that are more accurate for small object detection is developed and a coupled R-CNN method is proposed, which combines an AVPN and a vehicle attribute learning network to extract the vehicle's location and attributes simultaneously.

TL;DR: This paper designs a condensed backbone network, which consists of several dense blocks, and improves the cross-entropy loss to address the foreground–background imbalance and predict multi-scale ship proposals from several intermediate layers to improve the recall rate.

TL;DR: A discriminative cross-view subspace alignment algorithm where each view is represented by a subspace spanned by eigenvectors, which demonstrates that it is possible to use a scene category model or a scene attribute model learned on a set of ground view scenes for classification of VHR remote sensing images.

TL;DR: A novel pyramid binary pattern (PBP) feature is first proposed for IR pedestrian appearance representation and outperforms several state-of-the-art binary pattern features forIR pedestrian images classification, and is extended to 3D spatial-temporal volumes.