Hyperspectral image classification (HSIC) is a valuable method for identifying coastal wetland vegetation, but challenges like environmental complexity and difficulty in distinguishing land cover types make large-scale labeling difficult. Cross-domain few-shot learning (CDFSL) offers a potential solution to limited labeling. Existing CDFSL HSIC methods have made significant progress, but still face challenges like prototype deviation, covariate shifts, and rely on complex domain alignment (DA) methods. To address these issues, a feature reconstruction-based CDFSL (FRFSL) algorithm is proposed. Within FRFSL, a Prototype Calibration Module (PCM) is designed for the prototype deviation, which employs a Bayesian inference-enhanced Gaussian Mixture Model to select reliable query features for prototype reconstruction, aligning the prototypes more closely with the actual distribution. Additionally, a ridge regression closed-form solution is incorporated into the Distance Metric Module (DMM), employing a projection matrix for prototype reconstruction to mitigate covariate shifts between the support and query sets. Features from both source and target domains are reconstructed into dynamic graphs, transforming DA into a graph matching problem guided by optimal transport theory. A novel shared transport matrix implementation algorithm is developed to achieve lightweight and interpretable alignment. Extensive experiments on three self-constructed coastal wetland datasets and one public dataset show that FRFSL outperforms eleven state-of-the-art algorithms. The code will be available at https://github.com/Yqx-ACE/TIP_2025_FRFSL.

Event-based semantic segmentation has great potential in autonomous driving and robotics due to the advantages of event cameras, such as high dynamic range, low latency, and low power cost. Unfortunately, current artificial neural network (ANN)-based segmentation methods suffer from high computational demands, the requirements for image frames, and massive energy consumption, limiting their efficiency and application on resource-constrained edge/mobile platforms. To address these problems, we introduce SLTNet, a Spike-driven Lightweight Transformer-based Network designed for event-based semantic segmentation. Specifically, SLTNet is built on efficient spike-driven convolution blocks (SCBs) to extract rich semantic features while reducing the model’s parameters. Then, to enhance the long-range contextual feature interaction, we propose novel spike-driven transformer blocks (STBs) with binary mask operations. Based on these basic blocks, SLTNet employs a high-efficiency single-branch architecture while maintaining the low energy consumption of the Spiking Neural Network (SNN). Finally, extensive experiments on DDD17 and DSEC-Semantic datasets demonstrate that SLTNet outperforms state-of-the-art (SOTA) SNN-based methods by at most 9.06% and 9.39% mIoU, respectively, with extremely 4.58× lower energy consumption and 114 FPS inference speed. Our code is open-sourced and available at https://github.com/longxianlei/SLTNet-v1.0.

Event-based semantic segmentation in traffic scenes has attracted considerable attention in autonomous driving systems due to the advantages of event cameras such as high dynamic range, low latency, and low energy consumption. However, existing Artificial Neural Network (ANN)-based methods rely on conventional image frames, often neglecting the spatial-temporal dynamics inherent in event streams and consuming higher energy costs, significantly limiting their applicability in energy-constrained environments. In this study, we introduce Spike-BRGNet, a Spike-driven Boundary Region-Guided Network that efficiently extracts boundary information utilizing only events to guide the segmentation encoder, while preserving the energy efficiency of Spiking Neural Networks (SNNs). Specifically, to explore the implicit information from events, we design a three-branch spiking encoder that consists of semantic detail (SD), context aggregation (CA), and boundary aware (BA) branches to capture specific features. Then, a spiking multi-scale context aggregation (SMSCA) module is proposed to enhance the semantics of the CA branch. Finally, a novel boundary region-guided loss function and a dynamic surrogate gradient function, EvAF, are designed to optimize the model. Extensive experiments show that our model outperforms state-of-the-art (SOTA) SNN-based methods on DDD17 (+1.57%) and DSEC dataset (+1.91%). Furthermore, Spike-BRGNet consumes$17.76\times $less energy than ANN-based models, showing superior energy-saving performance.

Wetland is one of the three ecosystems in the world, and collaborative monitoring using hyperspectral images (HSIs) and light detection and ranging (LiDAR) has been important for wetland ecological protection. However, because of the domain shift of different images, cross-scene wetland classification of HSIs and LiDAR is a practical challenge, necessitating the development of models trained solely on the source domain (SD) and directly transferred to the target domain (TD) without retraining. To address this issue, an instance-wise domain generalization network (IDGnet) is proposed for HSI and LiDAR cross-scene wetland classification. An instance-wise random domain expansion module (IWR-DEM) is developed to simulate the domain shift, establishing the extended domain (ED). Specifically, the original HSI and LiDAR data are separated as semantic and background information in the frequency domain, a random background shift is applied to the HSI, and a semantic random shift is deployed to LiDAR. The HSI and LiDAR fusion features are extracted from the SD and ED by a weight-shared network. Multiple condition constraints are proposed for domain and class alignment, learning the domain-invariant and class-specific information and improving model generalization. Experiments conducted on two wetland datasets demonstrate the superiority of the proposed IDGnet for cross-scene wetland classification with HSI and LiDAR data. The codes will be available from the website:https://github.com/bigshot-g/IEEE_TGRS_IDGnet.

Accurate information on ground features is essential for the ecological protection and efficient management of this wetland. The resolution of satellite remote sensing images often falls short of the requirements for fine-scale classification. The Yellow River Estuary wetland is primarily vegetated, characterized by similar spectral curves. The use of images with limited spectral information additionally restricts the precision of classification. In response to the above issues, this study proposes a multi-branch fusion Transformer (MB Transformer) method based on high spatial resolution unmanned aerial vehicle (UAV) hyperspectral data. By constructing vegetation index features, dimensionality-reduced spectral features, and original spectral features, a multi-branch fusion Transformer model is used to classify in the Yellow River Estuary wetland at a fine scale. The results indicate that this method has obvious advantages in fine-scale classification of wetland in the Yellow River Estuary.

With the increasing demand for high precision and low power consumption in Human Activity Recognition (HAR) techniques, deep learning-based HAR models have emerged as the hottest research topics. Due to the excellent feature extraction and modeling abilities of deep learning models, which enable them to fit a wide variety of complex patterns. However, these models often require a large number of parameters, leading to high computational costs and longer processing time. These inherent factors pose significant challenges for resource-constraint edge devices to perform efficient HAR. To address these issues, we propose MobileHAR, which combines depthwise separable convolutions and novel Inverted Residual Inception Blocks (IRIB). This combination significantly reduces computational load and frequent memory access while maintaining high recognition accuracy. Then, we design a special class imbalance loss to supervise the model to pay more attention to imbalance classes. Finally, extensive experiments on several public datasets demonstrate that our method improves accuracy by 3.15% compared to traditional methods and requires only 0.15M parameters, which is at least four times fewer than the compared methods.

With the development of earth observation technology, hyperspectral image (HSI) and light detection and ranging (LiDAR) data collaborative monitoring has shown great potential in the ecological protection and restoration of coastal wetlands. However, due to the different working principle adopted by the HSI sensor and LiDAR sensor, the data obtained by them has different distribution characteristics. The distribution difference limits the fusion of HSI and LiDAR data, bringing a great challenge for coastal wetland classification. To tackle this problem, a multi-source feature embedding and interaction fusion network is proposed for coastal wetland classification, named MsFE-IFN. First, the HSI and LiDAR data are embedded in the same feature space, where the feature distribution of multi-source remote sensing are aligned to alleviate data distribution differences. Second, the aligned HSI and LiDAR features interact information in channels and pixels, which is able to establish the relationship of spectral, elevation and geospatial. Third, the HSI and LiDAR feature are sent into the feature fusion network, in which the low-frequency residual is retained to enrich intra-class features. Finally, the fused feature is applied for final class prediction. Experiments conducted on three coastal wetland HSI-LiDAR datasets created by ourselves demonstrate the superiority of the proposed MsFE-IFN for coastal wetland classification. The codes will be available from the website:https://github.com/bigshot-g/IEEE_TGRS_MsFE-IFN.

The transformer has become a prominent technique for hyperspectral image (HSI) classification, attributed to its capability to model global dependencies between features. Nevertheless, the predominant transformer-based methods rely on a direct information flow with a fixed number of tokens, causing the sequential transformer encoders to lack crucial interaction. This deficiency results in an inappropriate granularity of discriminative features and the loss of subtle patterns. In response to this limitation, we introduce a novel approach named Multi-level Class Token Transformer with Cross TokenMixer (MCTT) for HSI classification. Specifically, we explore a CNN stem network that incorporates 3D, 2D, and pointwise convolutions to encode local spatial-spectral information. The spectral-spatial features undergo transformation into semantic tokens using a semantic tokenizer. These tokens are then input into the transformer encoder to capture global interactions between different pixels. To create a hierarchical semantic representation, we propose a cross tokenmixer that integrates different levels of class tokens and patch tokens, enabling a multi-grained representation. The cross tokenmixers, with their varied number of tokens, facilitate the learning of distinct discriminative spectral-spatial representations and enable a comprehensive understanding of the HSI through a voting mechanism. Extensive experiments and ablation studies are conducted on three public HSI datasets to evaluate the performance of our proposed method. The results demonstrate the effectiveness and superior performance of our approach in HSI classification.

Sensor-rich smartphones have facilitated a lot of services and applications. Indoor/Outdoor (IO) status serves as a critical foundation for various upstream tasks, including seamless pedestrian navigation, power management, and activity recognition. Nevertheless, achieving robust, efficient, and accurate IO detection remains challenging due to environmental complexities and device heterogeneity. To tackle this challenge, some researchers have turned to deep learning for IO detection, which can deal with complex scenarios and achieve high detection accuracy. However, deep learning methods are often blamed for their expensive computational cost. Therefore, in this paper, we introduce a novel efficient IO detection method-DeepSIO, which can detect IO status accurately and efficiently. Specifically, different from existing IO detection methods, DeepSIO is developed based on spiking neural networks (SNN) that are more biologically plausible and computationally efficient than other deep neural networks. To better capture useful features, we propose to utilize dense connections between SNN layers. Extensive experiments are conducted in three typical scenarios, and experimental results demonstrate that DeepSIO outperforms state-of-the-art methods, achieving an accuracy of about 99.7%. Moreover, it has better generalization ability and can adapt well to new environments and devices.

With the increasing demand of observation, multi-source remote sensing data has been widely used. Hyperspectral Images (HSI) and Light Detection and Ranging (LiDAR) data have shown the great potential in land cover classification. However, the redundant information of multi-source data influences the effectiveness of heterogeneous data features, which reduces the accuracy of joint classification. To tackle this problem, a dual graph convolution joint dense networks is proposed for HSI and LiDAR classification. In this method, a dual graph convolution network (GCN)is extracted the spectral feature from euclidean graph and cosine graph, which contains the spectrum absolute and relative differences. A dense network is employed to acquire spatial feature from LiDAR data. Finally, a fully connected network fuses the spectral and spatial feature for classification. Experiments conducted on the Huston dataset demonstrate the effectiveness of the proposed method on joint classification.