Deep learning has garnered extensive attention in hyperspectral image (HSI) processing. However, its application in HSI semantic segmentation tasks has been relatively limited. Although segmentation methods can often interpret images up to two orders of magnitude faster than classification methods when interpreting images of the same scene, the segmentation task requires the training data to be fully labeled, i.e., each pixel has a corresponding label. Such data are scarce in HSI data. To address this problem, this article proposes a lightweight segmentation network based on a hierarchical attention segmentation network (LHAS), in which a generalized data augmentation (GDA) method is utilized to acquire relatively sufficient data for semantic segmentation. Specifically, the hierarchical attention module is designed to extract global and local information on HSI patches from different layers. A prototype auxiliary module (PAM) of cluster contrast has also been developed to enhance feature discrimination. Across two different datasets in various scenarios, the proposed LHAS demonstrates superior segmentation performance compared to existing methods, affirming its effectiveness. In addition, experiments conducted on embedded devices validate the efficacy of LHAS.

Nanopore selective sequencing allows the targeted sequencing of DNA of interest using computational approaches rather than experimental methods such as targeted multiplex polymerase chain reaction or hybridization capture. Compared to sequence-alignment strategies, deep learning (DL) models for classifying target and nontarget DNA provide large speed advantages. However, the relatively low accuracy of these DL-based tools hinders their application in nanopore selective sequencing. Here, we present a DL-based tool named ReadCurrent for nanopore selective sequencing, which takes electric currents as inputs. ReadCurrent employs a modified very deep convolutional neural network (VDCNN) architecture, enabling significantly lower computational costs for training and quicker inference compared to conventional VDCNN. We evaluated the performance of ReadCurrent across 10 nanopore sequencing datasets spanning human, yeasts, bacteria, and viruses. We observed that ReadCurrent achieved a mean accuracy of 98.57% for classification, outperforming four other DL-based selective sequencing methods. In experimental validation that selectively sequenced microbial DNA from human DNA, ReadCurrent achieved an enrichment ratio of 2.85, which was higher than the 2.7 ratio achieved by MinKNOW using the sequence-alignment strategy. In summary, ReadCurrent can rapidly classify target and nontarget DNA with high accuracy, providing an alternative in the toolbox for nanopore selective sequencing. ReadCurrent is available at https://github.com/Ming-Ni-Group/ReadCurrent.

Deep learning has received much attention in hyperspectral image (HSI) classification. However, most deep learning methods design relatively complex feature extraction and processing network modules for the characteristics of HSIs, which may not be necessary for relatively simple patch-based HSI classification tasks. The complex network structure and high feature channel dimension lead to large computational complexities, which limit the practical applicability of HSI. In this article, an elegant lightweight HSI classification-based information redistribution network (LIRnet) is proposed to separate and reaggregate the feature information to achieve feature information homogenization and extract discriminative feature information, respectively. The classification performance of LIRnet is better than that of existing methods on three different datasets in different scenarios, which proves its effectiveness. In addition, experiments on embedded devices verify the computational efficacy of LIRnet.

Deep learning has attracted much attention in hyperspectral image(HSI) classification. However, most deep learning methods ignore the information loss during spatial-spectral feature extraction, which potentially affects the classification performance. In this article, Mask Reconstruction-Based Decoupled Convolution Network(MrDCN) is proposed, which including the decoupled feature extraction module (DFEM) to extract spectral information and spatial information of target HSI patch respectively. The reconstruction modules are designed to maintain the feature extraction ability of DFEM and ensure that discriminative information in high-dimensional features and low-dimensional features is preserved. MrDCN outperforms state-of-the-art methods in classification on three datasets of various scenarios, which indicates its effectiveness, and experiments on embedded devices are executed to affirm the efficiency of MrDCN.

Deep learning has made many remarkable achievements in many fields but suffers from noisy labels in datasets. The state-of-the-art learning with noisy label method Co-teaching and Co-teaching+ confronts the noisy label by mutual-information between dual-network. However, the dual network always tends to convergent which would weaken the dual-network mechanism to resist the noisy labels. In this paper, we proposed a noise-tolerant framework named MLC in an end-to-end manner. It adjusts the dual-network with divergent regularization to ensure the effectiveness of the mechanism. In addition, we correct the label distribution according to the agreement between dual-networks. The proposed method can utilize the noisy data to improve the accuracy, generalization, and robustness of the network. We test the proposed method on the simulate noisy dataset MNIST, CIFAR-10, and the real-world noisy dataset Clothing1M. The experimental result shows that our method outperforms the previous state-of-the-art method. Besides, our method is network-free thus it is applicable to many tasks. Our code can be found at https://github.com/JiarunLiu/MLC.

Significant progress has been made in automatic road extra-ction or segmentation based on deep learning, but there are still margins to improve in terms of the completeness and connectivity of the results. This is mainly due to the challenges of large intra-class variances, ambiguous inter-class distinctions, and occlusions from shadows, trees, and buildings. Therefore, being able to perceive global context and model geometric information is essential to further improve the accuracy of road segmentation. In this paper, we design a novel dual-branch encoding block CoSwin which exploits the capability of global context modeling of Swin Transformer and that of local feature extraction of ResNet. Furthermore, we also propose a context-guided filter block named CFilter, which can filter out context-independent noisy features for better reconstructing of the details. We use CoSwin and CFilter in a U-shaped network architecture. Experiments on Massachusetts and CHN6-CUG datasets show that the proposed method outperforms other state-of-the-art methods on the metrics of F1, IoU, and OA. Further analysis reveals that the improvement in accuracy comes from better integrity and connectivity of segmented roads.

Hyperspectral image (HSI) classification has been widely used in remote sensing image analysis. Rich contextual information is beneficial to improve classification performance. Recently, plenty of attention-based CNN networks have been proposed for HSI classification due to the ability of the attention mechanism to perceive the global context. However, these methods have a high memory cost because the attention mechanism directly models each pair of pixel relationships, resulting in the size of the affinity matrix being very large. And it also failed to sufficiently leverage the relationship between pixels in HSI data. Regarding the problem, a lightweight and efficient end-to-end model is proposed for hyperspectral image classification in this paper. To efficiently extract global context, we propose a novel graph construction module, which shares the global attention map among all features and learns relations only between important features, thus saving computational overhead. In order to make better use of the context, we further aggregate the features relationships through the graph convolution module to achieve more accurate hyperspectral image classification. Experiments on three HSI datasets show it not only achieves better performance than other classification methods, but also improves calculation efficiency.