Land-use classification from remote sensing images has become an important but challenging task. This paper proposes Hierarchical Coding Vectors (HCV), a novel representation based on hierarchically coding structures, for scene level land-use classification. We stack multiple Bag of Visual Words (BOVW) coding layers and one Fisher coding layer to develop the hierarchical feature learning structure. In BOVW coding layers, we extract local descriptors from a geographical image with densely sampled interest points, and encode them using soft assignment (SA). The Fisher coding layer encodes those semi-local features with Fisher vectors (FV) and aggregates them to develop a final global representation. The graphical semantic information is refined by feeding the output of one layer into the next computation layer. HCV describes the geographical images through a high-level representation of richer semantic information by using a hierarchical coding structure. The experimental results on the 21-Class Land Use (LU) and RSSCN7 image databases indicate the effectiveness of the proposed HCV. Combined with the standard FV, our method (FV + HCV) achieves superior performance compared to the state-of-the-art methods on the two databases, obtaining the average classification accuracy of 91.5% on the LU database and 86.4% on the RSSCN7 database.

Rotation-invariant human detection is of vital significance to many applications, including victim detection via a daylight camera on an unmanned aerial vehicle (UAV). With this problem in mind, we propose a powerful rotation-invariant object-level description method, i.e., RIHOG-BoVWs, which is achieved by local-level feature calculation and high-level semantic feature extraction. In local-level feature calculation, we consider Rotation-invariant Histograms of Oriented Gradients (RIHOG) as the local descriptor and project gradient information into the frequency domain, where the rotation-invariant representation vector is constructed. In high-level semantic feature extraction, Bag of Visual Words (BoVWs) model is employed to achieve global description from local features without considering their spatial structures. Experimentally, we first confirm the sufficient discrimination power of the RIHOG-BoVWs on the public Freestyle Motocross dataset, and then demonstrate the high performance of RIHOG-BoVWs on a victim dataset which has varied backgrounds and body postures.

Biometric characteristics recognition (BCR) is a method of individual identification using inherent human vital signs or behavioral characteristics. This work aims at realizing remote BCR and target’s tracking system. First, we solve target’s identification through remote facial BCR. Then we achieve target’s vital signs recognition by non-contact breath and heartbeat detection. At last, we employ cross-validation of biometric characteristics recognition and vital signs identification technology method, and realize target’s tracking and locating.