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In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model's ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have a strong capacity to extract deep-level features from data. However, the standard convolution (SC) only considers the intensity information and ignores the spatial gradient information. Since spatial difference features are more robust to illumination invariance, this letter proposes a multiscale central difference convolutional (MSCDC) network for hyperspectral anomaly detection. Specifically, we use central difference convolution (CDC) to combine intensity and gradient information. This solution improves the representation ability of hyperspectral images (HSIs) and enhances the difference between the background and the anomalies. Furthermore, to fully utilize local spatial information and adapt to targets with different sizes, CDC kernels of three different sizes are used to capture high-, mid-, and low-level features, respectively. Finally, an SC is used to fuse multiscale features and obtain more reliable spatial information. Compared with five popular hyperspectral anomaly detection methods on four real-world HSI datasets, the proposed MSCDC exhibits excellent performance. [ABSTRACT FROM AUTHOR]

The large intra-class difference and inter-class similarity of scene images bring great challenges to the research of remote-sensing scene image classification. In recent years, many remote-sensing scene classification methods based on convolutional neural networks have been proposed. In order to improve the classification performance, many studies increase the width and depth of convolutional neural network to extract richer features, which increases the complexity of the model and reduces the running speed of the model. In order to solve this problem, a lightweight convolutional neural network based on hierarchical-wise convolution fusion (LCNN-HWCF) is proposed for remote-sensing scene image classification. Firstly, in the shallow layer of the neural network (groups 1–3), the proposed lightweight dimension-wise convolution (DWC) is utilized to extract the shallow features of remote-sensing images. Dimension-wise convolution is carried out in the three dimensions of width, depth and channel, and then, the convoluted features of the three dimensions are fused. Compared with traditional convolution, dimension-wise convolution has a lower number of parameters and computations. In the deep layer of the neural network (groups 4–7), the running speed of the network usually decreases due to the increase in the number of filters. Therefore, the hierarchical-wise convolution fusion module is designed to extract the deep features of remote-sensing images. Finally, the global average pooling layer, the fully connected layer and the Softmax function are used for classification. Using global average pooling before the fully connected layer can better preserve the spatial information of features. The proposed method achieves good classification results on UCM, RSSCN7, AID and NWPU datasets. The classification accuracy of the proposed LCNN-HWCF on the AID dataset (training:test = 2:8) and the NWPU dataset (training:test = 1:9), with great classification difficulty, reaches 95.76% and 94.53%, respectively. A series of experimental results show that compared with some state-of-the-art classification methods, the proposed method not only greatly reduces the number of network parameters but also ensures the classification accuracy and achieves a good trade-off between the model classification accuracy and running speed. [ABSTRACT FROM AUTHOR]

Deep learning methods, especially convolutional neural network (CNN)-based methods, have shown promising performance for hyperspectral image (HSI) change detection (CD). It is acknowledged widely that different spectral channels and spatial locations in input image patches may contribute differently to CD. However, they are treated equally in existing CNN-based approaches. To increase the accuracy of HSI CD, we propose an end-to-end Siamese CNN (SiamNet) with a spectral–spatial-wise attention (SSA-SiamNet) mechanism. The proposed SSA-SiamNet method can emphasize informative channels and locations and suppress less informative ones to refine the spectral–spatial features adaptively. Moreover, in the network training phase, the weighted contrastive loss function is used for more reliable separation of changed and unchanged pixels and to accelerate the convergence of the network. SSA-SiamNet was validated using four groups of bitemporal HSIs. The accuracy of CD using the SSA-SiamNet was found to be consistently greater than for ten benchmark methods. [ABSTRACT FROM AUTHOR]

Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on all but the most pristine density maps (<2.5 Å resolution). Here we introduce a deep learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict Cα atoms along a protein's backbone structure. The cascaded-CNN (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a protein's structure. This model predicts secondary structure elements (SSEs), backbone structure, and Cα atoms, combining the results of each to produce a complete prediction map. The cascaded-CNN is a semantic segmentation image classifier and was trained using thousands of simulated density maps. This method is largely automatic and only requires a recommended threshold value for each protein density map. A specialized tabu-search path walking algorithm was used to produce an initial backbone trace with Cα placements. A helix-refinement algorithm made further improvements to the α-helix SSEs of the backbone trace. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map protein sequences onto Cα traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6 Å and 4.4 Å resolution. It outperformed several state-of-the-art prediction methods including Rosetta de-novo, MAINMAST, and a Phenix based method by producing the most complete predicted protein structures, as measured by percentage of found Cα atoms. This method accurately predicted 88.9% (mean) of the Cα atoms within 3 Å of a protein's backbone structure surpassing the 66.8% mark achieved by the leading alternate method (Phenix based fully automatic method) on the same set of density maps. The C-CNN also achieved an average root-mean-square deviation (RMSD) of 1.24 Å on a set of 50 experimental density maps which was tested by the Phenix based fully automatic method. The source code and demo of this research has been published at https://github.com/DrDongSi/Ca-Backbone-Prediction. [ABSTRACT FROM AUTHOR]

Super-resolution mapping (SRM) is a technique to obtain sub-pixel resolution thematic map (SRTM). Soft-then-hard SRM (STHSRM) is an important SRM algorithm due to its simple physical meaning. The soft classification errors may affect the SRTM derived by STHSRM. To overcome this problem, the maximum a posteriori probability (MAP) super-resolution then hard classification (MTC) algorithm has been proposed. However, the prior information of the original image is difficult to utilize in MTC. To solve this issue, a novel method based on pansharpening then hard classification (PTC) is proposed to improve SRTM. The pansharpening technique is applied to the original coarse image to obtain the improved resolution image by suppling more prior information. The SRTM is then derived from the improved resolution image by hard classification. Not only does PTC inherit the advantages of MTC that avoids soft classification errors, but it can also incorporate more prior information from the original image into the process. Experiments based on real remote sensing images show that the proposed method can produce higher mapping accuracy than the STHSRM and MTC. It is shown that the PTC has the percentage correctly classified (PCC) in the range from 89.62% to 95.92% for the experimental dataset. [ABSTRACT FROM AUTHOR]