Hyperspectral images (HSIs) are characterized by high spatial resolution and are rich in spectral information. In the process of HSI classification, the extraction of spectral-spatial features directly influences the classification results. In recent years, the hyperspectral classification method based on convolutional neural networks has demonstrated excellent performance. However, as the network structure deepens, degradation occurs, and the features learned from the fixed-scale convolutional kernels are usually specific, which is not conducive to feature learning and thus impairs the classification accuracy. To solve the problem of difficult extraction of features and underutilization of information from HSI data, a densely connected multiscale attention network based on 3-D convolution is proposed for HSI classification. First, to reduce the spectral redundancy of the HSIs, the principal component analysis algorithm is performed on the raw HSI data; then, several multiscale blocks comprised of parallel factorized spatial-spectral convolution modules of different sizes are adopted to extract the enriched spectral-spatial features from HSIs; furthermore, dense connections are introduced to further fuse features obtained from blocks of different depths, thereby enhancing feature reuse and propagation and helping to alleviate the problem of vanishing gradients. Besides, the channel-spectral-spatial attention block is put forward to spontaneously reweight the fused features to emphasize the features that are more relevant to the classification results while weakening the less relevant ones. The experimental results show that the proposed method is effective in extracting discriminative features of the target and outperforms the other state-of-the-art methods.

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Understanding the interaction of ionized two-dimensional (2D) graphene oxide (GO) nanosheets at the air–water interface can improve the self-assembly framework of GO sheets. Herein, the potential of mean force (PMF) was simulated to study the edge-to-edge interacting mode between two deprotonated GO nanosheets at the air–water interface. The PMF profile displays a unique phenomenon that ionized GO nanosheets with the same negative charge can attract each other at the interface, which is obviously in contrast with the repulsive interaction in the bulk phase. The attractive interaction between the negatively charged GOs at the interfaces can be attributed to an enhanced solvation interacting force arising from the bridge water structure shared by the two single-layer ionized GO sheets. More specifically, the novel interfacial interaction is highly correlated with the charged nature of GO sheets and their special parallel packing arrangement at the interface. Our simulation result, for the first time, provides new physical chemistry insights into the interface-mediated solvation force between charged 2D nanoparticles.

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