Remote sensing image scene classification is an important method for understanding the high-resolution remote sensing images. Based on Convolutional Neural Network, various classification methods have been applied into this field and achieved remarkable results. These methods mainly rely on the semantic information to improve the classification performance. However, as the network goes deeper, the highly abstract and global semantic information makes it difficult for the network to accurately classify scene images with similar layout and structures, limiting the further improvement of classification accuracy. Relying on the semantic information only is not sufficient to effectively classify these similar scene images and the network needs spatial information to enhance the classification capability. To solve this dilemma, this paper proposes a best representation branch model, which reaches the optimal balance point where the network can make use of both the semantic information and spatial information to improve the final classification accuracy. In the proposed method, ResNet50 pretrained on the ImageNet dataset is first divided into four branches with different depths to extract feature maps and Capsule Network is used as the classifier. The Grad-CAM algorithm is adopted to explain the mechanism of the optimal balance point from the perspective of attention and guide the further feature fusion. In addition, ablation studies are conducted to prove the effectiveness of our method and extensive experiments are conducted on three public benchmark remote sensing datasets. The results demonstrate that the proposed method can achieve competitive classification performance compared to the state-of-the-art methods.

In the process of bone defective reparation and engineered bone tissue construction, osteoblasts are adhered to the surface of the scaffold materials and impart the external mechanical load to the osteoblasts. So, the dynamic mechanical property of the scaffolds play an important role in the bone tissue repair and it is valuable to research. Material type and the architectural design of scaffolds are also important to facilitate cell and tissue growth. The aim of this study was to prepare a kind of material with good pore connectivity and analyze its dynamic mechanical property. Fabrication and characterization of micro-hydroxyapatite(m-HA)/chitosan(CS) polymer composite scaffolds with well interconnected spherical pore architectures were reports. Micro-HA was prepared by being calcined and ball milled. Paraffin spheres in the range of 160–330 µm were fabricated with a dispersion method and used as the porogen in the fabrication of the scaffolds. Polymer scaffolds were fabricated by the technique of compression molding and particulate leaching method. The effects of the porogen content on the properties of the scaffolds were studied. With the increase of porogen, the pore of the scaffolds increased and became interconnected. Cyclic loading of three scaffolds were tested with 10 % strain under four levels of loading frequency, 0.1, 0.5, 1 and 1.5 Hz. The porous composite scaffolds exhibited a viscosity-elastic behaviour with a maximum stress of 3–4 kPa. At each frequency, modulus value is decreased with the paraffin microspheres content, but there was no significance difference in the peak stress of the three samples. All the samples tested displayed clear hysteresis loops. There was no significance difference in the peak hysteresis of the three samples, and the hysteresis difference values between the sixth compression cycle and the initial cycle for three samples was similar, with no statistically significant differences. Micro-HA/CS composite scaffolds with interconnected spherical macropores were fabricated using pherical paraffin as porogen. The porous composite scaffolds exhibited a viscosity-elastic behaviour with good repeatability. It is benefit to study the influence of the mechanical load on the cell of the scaffold.

At present, the classification accuracy of high-resolution Remote Sensing Image Scene Classification (RSISC) has reached a quite high level on standard datasets. However, when coming to practical application, the intrinsic noise of satellite sensors and the disturbance of atmospheric environment often degrade real Remote Sensing (RS) images. It introduces defects to them, which affects the performance and reduces the robustness of RSISC methods. Moreover, due to the restriction of memory and power consumption, the methods also need a small number of parameters and fast computing speed to be implemented on small portable systems such as unmanned aerial vehicles. In this paper, a Lightweight Progressive Inpainting Network (LPIN) and a novel combined approach of LPIN and the existing RSISC methods are proposed to improve the robustness of RSISC tasks and satisfy the requirement of methods on portable systems. The defects in real RS images are inpainted by LPIN to provide a purified input for classification. With the combined approach, the classification accuracy on RS images with defects can be improved to the original level of those without defects. The LPIN is designed on the consideration of lightweight model. Measures are adopted to ensure a high gradient transmission efficiency while reducing the number of network parameters. Multiple loss functions are used to get reasonable and realistic inpainting results. Extensive tests of image inpainting of LPIN and classification tests with the combined approach on NWPU-RESISC45, UC Merced Land-Use and AID datasets are carried out which indicate that the LPIN achieves a state-of-the-art inpainting quality with less parameters and a faster inpainting speed. Furthermore, the combined approach keeps the comparable classification accuracy level on RS images with defects as that without defects, which will improve the robustness of high-resolution RSISC tasks.