Domain generalization (DG) has shown significant potential for cross-scene hyperspectral image (HSI) classification, wherein a model is trained exclusively on the source domain (SD) and can be directly transferred to an unseen target domain (TD). Current DG-based methods focus only on expanding the distribution of source domains by randomizing the style of the entire HSI cube. They fail to account for the domain shift problem caused by the variance of spatial land-cover distribution (context semantics), which results in SD-specific patterns being overly emphasized during training and, consequently, limiting the generalizability. Moreover, such randomization on cubes may introduce undesirable artifacts, such as blurring or distortion, leading to semantically compromised samples. In this paper, aFourier-based spectral-spatial generator(FSSG) is proposed to generate diversified and robust generative domain (GD). Specifically, a Fourier disentanglement is developed to construct spectral expansion (SpeE) and spatial expansion (SpaE) from pixel-wise and region-wise levels, respectively. In SpeE, the style information is transmitted across pixels in a privacy-protecting way, i.e., SD shares the semantic information with the GD. In SpaE, an effective continuous frequency space interpolation is employed to transmit the styles and semantics information across cubes, which enables GD to bridge inter-domain gaps in both styles and context semantics. To further alleviate the over-emphasis on SD-specific patterns, a relaxation procedure is integrated within an adversarial training based on a coarse-to-fine paradigm, which facilitates the HSI cubes to gain more robust context semantics. Extensive experiments and analyses, conducted with two baseline methods across three public datasets, demonstrate the superiority of the proposed approach.

Cross-scene hyperspectral image classification faces significant challenges due to spectral-spatial domain shifts. Existing domain generalization (DG) methods typically generate extended domains (EDs) to enhance the diversity of source domains (SDs) and extract domain-invariant features for target domain classification. However, these methods often struggle to balance the invariance and diversity of ED features. For instance, placing excessive emphasis on diversity can compromise cross-domain invariance. Additionally, they tend to overlook domain-specific features that are crucial for classification, which limits generalization performance. To address these challenges, this paper proposes an ensemble network based on Fourier phase preservation and spectral random augmentation (FSENet), which generates high-quality EDs and effectively integrates domain-invariant and domain-specific features to improve classification accuracy. FSENet consists of three key components: 1) the Fourier Spatial Augmentation Module, which preserves phase while perturbing amplitude to enhance both the invariance and diversity of spatial features; 2) the Random-augmented Spectral Channel Attention Module, which employs channel attention and random perturbations to boost the discriminative ability of spectral features; and 3) the Domain Label-guided Ensemble Framework, which utilizes a multi-branch architecture to combine domain-invariant and domain-specific features from multiple SDs, overcoming the limitations of single-feature representations. Experiments on four real-world hyperspectral datasets, including Houston, Pavia, HyRANK and WHU, demonstrate that FSENet achieves a cross-scene classification accuracy improvement of 2.27% – 4.74% compared to state-of-the-art domain adaptation and DG methods. Ablation studies confirm the effectiveness of each proposed module. The code of this work will be available at https://github.com/goesfor/FSENet.

Hyperspectral image (HSI) classification is an important topic in remote sensing tasks, aiming to exploit the spectral and spatial information in HSI to assign category labels to each pixel. Recently, numerous deep learning (DL) methods such as convolutional neural network (CNN) and transformer, have been introduced and achieved promising results. These DL-based models tend to use patch-based input patterns, where a patch is extracted around a central pixel and the neighboring pixels serve as auxiliary roles. However, compared to developing novel models, characteristics of the patch-based HSI classification framework have not been adequately explored. Most existing patch-based DL methods tend to overlook the potential relationships between the central pixel and its neighboring pixels. In addition, the unique spectral characteristics of HSI (e.g. adjacent bands possess higher correlation) require special attention. Moreover, the design of feature extractors for HSIs appears to be constrained primarily to the spatial and spectral domains. To cope with the above problems, we propose an HSI classification specifically designed model (HSIC-SDM), which encompasses three branches—frequency feature learning, spatial feature learning and spectral feature learning. The frequency feature learning branch employs a Fourier-based feature extractor to acquire representations in the frequency domain. The spatial feature learning branch emphasizes the significance of central pixel and edge information. The spectral feature learning branch concerns the high correlations in the adjacent spectral bands. Multidomain features derived from three branches provide diverse perspectives for the final classification. Extensive experiments on three datasets demonstrate the superiority of the proposed method over the state-of-the-art (SOTA) compared methods. The code of our work will be available publicly at https://github.com/Tikiten/HSIC-SDM.

To enable the operator to control the surgical assistant robot without a separate joystick, we aim to develop an interface using artificial intelligence that responds to the movement of surgical instruments in the laparoscopic screen. In this study, we propose a method for detecting and tracking surgical tools using the YOLOv5 (YOU ONLY LOOK ONCE v5) and DeepSORT (Deep Learning based Simple Online and Real-time Tracking) algorithms. The proposed approach employs the YOLOv5 algorithm to detect surgical tools in real-time, and the DeepSORT algorithm to track the detected tools across multiple frames. The YOLOv5 model requires minimal computation, which enables the rapid detection of surgical instruments. Furthermore, the DeepSORT algorithm can precisely track object movements in complex environments. The proposed method’s tracking stability was assessed using the average pixel error performance metric test. To evaluate the method’s performance, we used a 2-degree-of-ffeedom remote center motion experimental setup and employed the PID control algorithm to control the laparoscopic camera’s movements.

As a foundation task of natural language processing, text classification is widely used in information retrieval, public opinion analysis, and other related tasks.Facing the problem of sparse features of Chinese short texts, which affects the classification accuracy of Chinese short texts, this paper proposes a Chinese short text classification method based on the Character Frequency Sub-word Enhancement (CFSE), which can effectively improve the classification accuracy of Chinese short texts.First, the initial Chinese-character sequence is mapped to the corresponding Character Frequency Sub-word (CFS) sequence based on the global character 1 frequency information.Second, the relationship features among data are extracted based on BiLSTM-Att processing CFS sequence, and the semantic features of the initial Chinese-character sequence are obtained through ERNIE.Finally, these two kinds of features are fused and input into the text classifier to obtain the classification results.Experimental results show that the proposed method can improve the classification accuracy of Chinese short texts.

Convolutional neural networks (CNNs), a kind of feedforward neural network with a deep structure, are one of the representative methods in hyperspectral image (HSI) classification. However, redundant information and interclass interference are common and challenging problems in HSI classification. In addition, if the spectral and spatial information is not properly extracted and analyzed, it will affect the classification performance of the network to a great extent. Aiming at these issues, this article proposes an HSI classification method based on an adaptive hash attention mechanism and a lower triangular network (AHA-LT). First, the attention mechanism is introduced in the preprocessing stage, which is composed of the spectral attention module and the adaptive hash spatial attention module in series. Then, the data processed by the attention mechanism are introduced into the lower triangular network (LTNet) to obtain the fused high-dimensional semantic features. Finally, we compress the features and obtain the output classification results through several fully connected layers. Among them, LTNet is composed of 2-D–3-D CNN and multiscale features. The network integrates the characteristics of multibranch, feature fusion, feature compression, and skip connections. Extensive experiments on four widely used HSI data sets show that the proposed method can obtain a great improvement in performance compared with the existing methods.

Graph-based semisupervised hyperspectral image (HSI) classification methods have obtained extensive attention. In graph-based methods, a graph is first constructed, and then the label propagation is carried out on the constructed graph to obtain the labels for unknown samples. However, the results of label propagation may be unreliable, especially in the case of very limited labeled samples. To address the above problem, we propose dual sparse representation graph-based collaborative propagation (DSRG-CP) for HSI classification. Specifically, DSRG-CP adopts sparse representation (SR) to construct spectral and spatial graphs on spectral and spatial dimensions, respectively. Then, label propagation is performed on two graphs iteratively. In each iteration, only the samples with high classification confidence from one graph are added into another graph as labeled samples for the next label propagation. After several iterations, the labels of unlabeled samples are predicted by fusing the results of label propagation from two graphs. In addition, to make the spectral graph more discriminative, the regularizer of spectral statistical information is added into spectral SR model. To make the classification results more consistent in space, the superpixel block constraint is added into spatial graph model as regularizer. To evaluate the performance of the proposed method, DSRG-CP is compared with several graph-based methods and other state-of-the-art methods. Extensive experiments on real HSI data sets show that DSRG-CP can obtain competitive results for HSI classification.

Ensemble learning is an effective approach for improving the generalization ability of base classifiers. To generate a set of accurate and diverse base classifiers, different data perturbation schemes have been proposed. For instance, Bagging perturbs the training data via bootstrap sampling. However, when a stable learning algorithm (e.g., KNN, Naive Bayes) is used to train base classifiers, the sole perturbation on the training data may not produce diverse base classifiers. In this paper, by using the attribute reduction technology in rough sets, a multi-modal perturbation-based algorithm (called ‘E _ EARBS’) is proposed for the ensemble of base classifiers. E _ EARBS simultaneously perturbs the feature space, training data and learning parameters, where the relative decision entropy(RDE)-based approximate reducts are used to perturb the feature space, and bootstrap sampling is used to perturb the training data. Experimental results show that E _ EARBS can provide competitive solutions for ensemble learning.

Saliency prediction is an important way to understand human's behavior and has a wide range of applications. Although lots of algorithms have been designed to predict saliency for planar images, there are few works for 360° images. In this paper, we propose an encoder-decoder network for panoramic image saliency prediction. Dilated convolutional layers are deployed in the network, which can extract more representative features and improve the accuracy of saliency prediction. To deal with the image distortions in 360° images, our network takes cube map format as input and processes six faces of cube map simultaneously. Respecting the saliency distribution of ground truth, we also propose a new data augmentation method to train the network, which is validated to be helpful for performance improvement. Extensive experiments show that our method gives new state-of-the-art results on 360° image saliency prediction.

In this paper, a new change detection method based on fully-connected conditional random field (FCCRF) with region potential is proposed. To deal with over-smoothing problem in FCCRF model, we propose to add region boundary constraint into FCCRF model. The proposed method defines the unary potential using the memberships of unsupervised fuzzy C-means clustering, designs the pairwise potential by a linear combination of Gaussian kernels using the complete set of pixels in the multi-temporal images to suppress noise effects, implements the region potential by the mean probability of pixels within image objects to preserve details of object boundary information. Experimental results demonstrate that the proposed method improves the change detection accuracy, turns out to be more robust against noise than traditional approaches.