Sea ice is one of the typical causes of marine disasters. Sea ice image classification is an important component of sea ice detection. Optical data contain rich spectral information, but they do not allow one to easily distinguish between ground objects with a similar spectrum and foreign objects with the same spectrum. Synthetic aperture radar (SAR) data contain rich texture information, but the data usually have a single source. The limitation of single-source data is that they do not allow for further improvements of the accuracy of remote sensing sea ice classification. In this paper, we propose a method for sea ice image classification based on deep learning and heterogeneous data fusion. Utilizing the advantages of convolutional neural networks (CNNs) in terms of depth feature extraction, we designed a deep learning network structure for SAR and optical images and achieve sea ice image classification through feature extraction and a feature-level fusion of heterogeneous data. For the SAR images, the improved spatial pyramid pooling (SPP) network was used and texture information on sea ice at different scales was extracted by depth. For the optical data, multi-level feature information on sea ice such as spatial and spectral information on different types of sea ice was extracted through a path aggregation network (PANet), which enabled low-level features to be fully utilized due to the gradual feature extraction of the convolution neural network. In order to verify the effectiveness of the method, two sets of heterogeneous sentinel satellite data were used for sea ice classification in the Hudson Bay area. The experimental results show that compared with the typical image classification methods and other heterogeneous data fusion methods, the method proposed in this paper fully integrates multi-scale and multi-level texture and spectral information from heterogeneous data and achieves a better classification effect (96.61%, 95.69%).
Highly efficient electromagnetic wave absorbers are urgently demanded for the removal of electromagnetic pollution and interference in consumers’ electronics. ABO3 perovskite oxides with fascinating optoelectronic properties are potential electromagnetic wave absorbers. In this work, we developed a bi-metal–organic framework-derived perovskite LaCoO3/Co3O4 nanocomplex by controlling the calcination temperature and heating rate in air. It exhibited excellent electromagnetic wave absorption performance, i.e., a broad absorption bandwidth of 5.93 GHz (reflection loss lower than −10 dB), which was superior to almost all of the ABO3 type perovskite absorbers. It will open a new opportunity for the application of emerging perovskite oxides in electromagnetic functional materials.
The results of this article show that 2D-CNN has great potential in the field of soil recognition and classification combine with LIBS, and provides a new and reliable data processing method for LIBS to classify materials with similar chemical properties.
In this study, we investigate a low-Weber-number flow of a liquid curtain bridged between two vertical edge guides and the upper surface of a moving substrate. Surface waves are observed on the liquid curtain, which are generated due to a large pressure difference between the inner and outer region of the meniscus on the substrate, and propagate upstream. They are categorized as varicose waves that propagate upstream on the curtain and become stationary because of the downstream flow. The Kistler's equation, which governs the flow in thin liquid curtains, is solved under the downstream boundary conditions, and the numerical solutions are studied carefully. The solutions are categorized into three cases depending on the boundary conditions. The stability of the varicose waves is also discussed as wavelets were observed on these waves. The two types of modes staggered and peak-valley patterns are considered in the present study, and they depend on the Reynolds number, the Weber number, and the amplitude of the surface waves. The former is observed in our experiment, while the latter is predicted by our calculation. Both the types of modes can be derived using the equations with periodic coefficients that originated from the periodic base flow due to the varicose waves. The stability analysis of the waves shows that the appearance of the peak-valley pattern requires a significantly greater amplitude of the waves, and a significantly higher Weber number and Reynolds number compared to the condition in which the staggered pattern is observed.
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