Feature-based methods have been developed in the past decades for the registration of optical satellite images. However, it is still a challenging problem to handle well the registration between medium and high spatial resolution images due to the large difference of the spatial structural features and local details for the same objects. In this study, an automated co-registration technique is proposed that integrates an improved SIFT (I-SIFT) and a novel matching strategy called spatial consistency constraints (SCC) to cope with the large difference in spatial resolutions between the image pair. Three constraints on angle, distance, and ratio are introduced to re the initial matching features obtained by I-SIFT. Three groups of experiments were conducted to validate the effectiveness of the proposed method. The experiments used high resolution multispectral and panoramic SPOT 5/6 images and Landsat 5/8 orthorectification images. Experimental results show that the registration error lies in about 1 pixel of high-resolution images and demonstrate that the proposed I-SIFT-SCC approach is suitable for fine registration of optical satellite images from medium spatial resolution to high spatial resolution with resolution ratio up to 6. 相似文献
Because of heat amount is different from peripheral to central of friction welding interface, which is leaded to vary the characterizations along that interface. Current study, respectively, focused on the effect of different friction pressure on micro-structural and mechanical properties of that friction welding joint interface. Presently, these friction pressures are 110, 130, 150 and 170 MPa while kept all other conditions constant. The effects of different friction pressure on welding interface characterization were investigated by EDX, SEM, tensile, compression, impact and hardness tests. The tensile tests carried out on the standardized test piece with diameter 6 mm and 8 mm, thus, compression tests were extracted from the positions of 0°, 45° 90° with test specimen of 4 mm diameter and 6.5 mm length at weld center. Whereas, the impact test pieces were picked up in two positions, the first one is symmetrical, which it obtained to the respect of the rotation axis and the interface, on the other hand, the second one is non-symmetrical with the axis of rotation and symmetrical to the interface, for making the notch head coincide with the center of the welded joint, The obtained results showed that with reducing of friction pressure will present lack of bonding increasing from peripheral toward the welding center, which will responsible on reducing of the mechanical properties such as tensile, compression and impact strength. 相似文献
World Wide Web - Infectious diseases such as Influenza and Ebola pose a serious threat to everyone but certain demographics and cohorts face a higher risk of infection than others. This research... 相似文献
Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANNFCM-GRN. In MANNFCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANNFCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANNFCM-GRN and show that MANNFCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANNFCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.