Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).

Five series of 37 new acylate and epoxide derivatives (3-39) of Euphorbia factor L3, a lathyrol diterpene isolated from Euphorbia lathyris, were designed by modifying the hydroxyl moiety of C-3, C-5, or C-15. Chemoreversal effects of the acylates on multidrug resistance (MDR) were evaluated in breast cancer multidrug-resistant MCF-7/ADR cells that overexpress P-glycoprotein (P-gp). Eight derivatives exhibited greater chemoreversal ability than verapamil (VRP) against adriamycin (ADR) resistance. Compounds 19 and 25 exhibited 4.8 and 4.0 times, respectively, more effective reversal ability than VRP against ADR resistance. To determine the key characteristics of Euphorbia factor L3 derivatives that contribute to MDR reversal, we conducted a structure-activity relationship study of these compounds. The simulation studies indicated different possible mechanisms and revealed the important influence of hydrophobic interactions and hydrogen bonds in the flexible cavity of P-gp.

Object detection is a focal point in remote sensing applications. Remote sensing images typically contains a large number of small objects and a wide range of orientations across objects. This results in great challenges to small object detection approaches based on remote sensing images. Methods directly employ channel relations with equal weights to construct information features leads to inadequate feature representation in complex image small object detection tasks. Multi-scale detection methods improve the speed and accuracy of detection, while small objects themselves contain limited information, and the features are easily lost following down-sampling. During the detection, the feature images are independent across scales, resulting in discontinuity at the detection scale. In this paper, we propose the Multi-Scale Context and enhanced Channel Attention (MSCCA) model. MSCCA employs PeleeNet as the backbone network. In particular, the feature image channel attention is enhanced and the multi-scale context information is fused with multi-scale detection methods to improve the characterization ability of the convolutional neural network. The proposed MSCCA method is evaluated on two real datasets. Results show that for 512 × 512 input images, MSCCA was able to achieve 80.4% and 94.4% mAP on the DOTA and NWPU VHR-10, respectively. Meanwhile, the model size of MSCCA is 21% smaller than that of its predecessor. MSCCA can be considered as a practical lightweight oriented object detection model in remote sensing images.

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