Ship detection in multispectral remote-sensing images is critical in marine surveillance applications. The previously proposed ship-detection methods for multispectral satellite imagery usually work well under ideal conditions. When meeting complex environments such as shadows, mists, or clouds, they fail to detect ships. To solve this problem, we propose a novel spectral-reflectance-based ship-detection method. Research has shown that different materials have unique reflectance curves in the same spectral wavelength range. Based on this observation, we present a new feature using the reflectance gradient across multispectral bands. Moreover, we propose a neural network called lightweight fusion networks (LFNet). This network combines the aforementioned reflectance and the color information of multispectral images to jointly verify the regions with ships. The method utilizes a coarse-to-fine detection framework because of the large-sense-sparse-targets situation in remote-sensing images. In the coarse stage, the proposed reflectance feature vector is used to input the classifier to rule out the regions without ships. In fine detection, the LFNet is used to verify true ships. Compared with some traditional methods that merely depend on appearance features in images, the proposed method takes advantage of employing the reflectance variance in objects between each band as additional information. Extensive experiments have been conducted on multispectral images from four satellites under different weather and environmental conditions to demonstrate the effectiveness and efficiency of the proposed method. The results show that our method can still achieve good performance even under harsh weather conditions.

Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting. Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets. Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios. Significance. FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility.

Human–computer interaction (HCI) plays a significant role in modern education, and emotion recognition is essential in the field of HCI. The potential of emotion recognition in education remains to be explored. Confusion is the primary cognitive emotion during learning and significantly affects student engagement. Recent studies show that electroencephalogram (EEG) signals, obtained through electrodes placed on the scalp, are valuable for studying brain activity and identifying emotions. In this paper, we propose a fusion framework for confusion analysis in learning based on EEG signals, combining feature extraction and temporal self-attention. This framework capitalizes on the strengths of traditional feature extraction and deep-learning techniques, integrating local time-frequency features and global representation capabilities. We acquire localized time-frequency features by partitioning EEG samples into time slices and extracting Power Spectral Density (PSD) features. We introduce the Transformer architecture to capture the comprehensive EEG characteristics and utilize a multi-head self-attention mechanism to extract the global dependencies among the time slices. Subsequently, we employ a classification module based on a fully connected layer to classify confusion emotions accurately. To assess the effectiveness of our method in the educational cognitive domain, we conduct thorough experiments on a public dataset CAL, designed for confusion analysis during the learning process. In both subject-dependent and subject-independent experiments, our method attained an accuracy/F1 score of 90.94%/0.94 and 66.08%/0.65 for the binary classification task and an accuracy/F1 score of 87.59%/0.87 and 41.28%/0.41 for the four-class classification task. It demonstrated superior performance and stronger generalization capabilities than traditional machine learning classifiers and end-to-end methods. The evidence demonstrates that our proposed framework is effective and feasible in recognizing cognitive emotions.

The expanded availability of remote sensing data in recent years has yielded an increasing demand for advanced object detection methods to support Earth observation applications. Vision Transformers (ViTs) have recently emerged as a powerful backbone in the field of visual recognition, achieving exceptional performance in various tasks. However, adapting the pretrained ViTs to specific downstream tasks, particularly in remote sensing, represents a challenge due to the different distribution between the pretraining and downstream task datasets. To address this challenge, this paper introduces the distribution bias embedding-based tuning (DBET) method. DBET integrates a bias embedding token, which dynamically adjusts the feature representations to better align the distribution bias between the pretraining and downstream task datasets. DBET also reduces the number of parameters that require fine-tuning, thus significantly improving computational efficiency and reducing training time while maintaining the original structure unchanged. DBET is verified by experimental evaluations on benchmark datasets, including the DOTA-v1.0, RarePlanes, and EAGLE datasets. The results demonstrate that DBET can not only reduce the number of trainable parameters and accelerate the fine-tuning process but can also significantly improve accuracy, particularly for tasks with a limited number of categories, providing a scalable solution for remote sensing object detection. The DBET method achieved a recognition accuracy of 56.94% on the EAGLE dataset, 80.56% on the five-class RarePlanes dataset, and 70.68% on the DOTA-v1.0 dataset. Moreover, the DBET method reduced the average training time to 58% of full fine-tuning method.

Many essential services for autonomous vehicles, e.g., navigation on high-quality maps, are designed based on the understanding of traffic conditions, e.g., travel time/speed on road segments, traffic flow, etc. However, most existing traffic condition models lack the consideration of the differentiation for vehicles with different types (e.g., personal vehicles or trucks) and thus they cannot satisfy some type-specific services, e.g., traffic-condition-based routing for autonomous vehicles with different types. To address this challenge, we design a novel vehicular mobility based sensing model called mDrive to predict the travel speed on the road segments, which is targeted for different types of vehicles by utilizing the camera data obtained from the traffic cameras equipped in the road intersections only, without any in-vehicle GPS devices. mDrive addresses the type-aware traffic speed prediction problem with sparse sensors based on three correlations: (1) the spatial correlation of travel speed on the connected road segments; (2) the temporal correlation of travel speed on the consecutive time slots; (3) the type correlation of different vehicular types’ speed on the same road segment. We implement mDrive on traffic camera data from the Chinese city Suzhou and evaluate it by using the detailed GPS data from personal vehicles, taxis, and trucks, with road contextual data as ground truth. The experiment show mDrive outperforms state-of-the-art methods by reducing 6.2% mean relative error on average for all types of vehicles.