Hyperspectral image (HSI) data possess complex spatial structures and high-dimensional spectral information. Mamba has been applied to address the limitations of general methods in HSI classification, including restricted receptive fields and high computational complexity. However, the scan mechanism of traditional Mamba unreasonably constructs spatial distance relationships between neighboring row pixels and fails to adaptively construct the optimal scanning path based on the spectral similarity of pixels. Additionally, the characteristic of traditional Mamba scanning each channel independently overlooks the feature extraction from high-dimensional spectral information. This work proposes a Spectral State Fusion Tree Mamba (SSFTM) architecture to resolve these limitations. The Tree Scan (TS) mechanism computes cosine distances among spatial neighboring pixels and spectral channels to construct adaptive minimum spanning trees in both the spatial and spectral domains, thereby establishing reasonable spatial-spectral relationships and enabling efficient joint feature extraction. The Spectral State Fusion (SSF) mechanism applies multi-layer one-dimensional dilated convolutions along the spectral dimension to the state space vectors, enabling inter-channel interaction and promoting multi-scale spectral feature extraction. The proposed SSFTM demonstrates superior classification accuracy across multiple datasets compared to SOTA methods and exhibits acceptable computational complexity. The code is available at https://github.com/copawloroous/SSFTM.

Transformers have gained significant attention in multimodal remote sensing fusion due to their strong global context modeling capability. Although Transformer-based methods excel at processing high-dimensional spectral sequences and joint spatial-spectral information, most current research remains focused on the spatial domain. Consequently, the exploration of frequency-domain features—particularly implicit frequency representations—is often neglected. Moreover, efficiently fusing multimodal data features while emphasizing more discriminative information remains a challenging task. To address these challenges, this paper proposes an Attention-guided Frequency Feature Decomposition Network (AF2DN) for Hyperspectral and LiDAR Data Classification. First, a Transformer-based Frequency Feature Decomposition(TFFD) method is proposed, employing window attention to capture distinct directional frequency components from multimodal remote sensing data. Through this approach, low-frequency components are utilized to characterize global structural information, while various high-frequency components are employed to extract diverse texture and edge features. Second, an Attention Frequency Modulation(AFM) module is developed, incorporating a weight learning matrix in the frequency domain. This matrix is designed to selectively amplify and suppress different frequency components, thereby reducing data redundancy resulting from frequency feature decomposition. Finally, an adaptive Multimodal Same-Frequency Feature Fusion (AMSF3) module is designed to achieve cross-modal feature integration at identical frequency bands. Extensive experiments are conducted on three benchmark datasets, and the results demonstrate that the proposed framework outperforms existing state-of-the-art methods while exhibiting stronger adaptability in complex environments.

The multisource remote sensing (RS) data have yielded promising results in target detection and classification tasks. However, most existing methods primarily focus on the spatial features inherent in spectral information, while the continuous spectral characteristics are generally neglected. This oversight leads to insufficient extraction of spectral information, thereby limiting detection performance. Recently, the Mamba architecture, based on state space models (SSMs), integrates the advantages of long-range sequence modeling and linear computational efficiency, demonstrating significant potential in low-dimensional scenarios. Inspired by this, we propose Slice Mamba for multisource RS data fusion classification. Specifically, we design two scanning methods: lateral slice scanning (LatSS) and longitudinal slice scanning (LonSS), which construct sequences from lateral and longitudinal perspectives to facilitate information interaction between pixels. In conjunction with the Mamba architecture, we develop the lateral slice Mamba block (LatSMB) and the longitudinal slice Mamba block (LonSMB) to capture continuous spatial-spectral features. Based on this, we establish the slice feature extraction (SFE) module for extracting spatial-spectral feature information and design the cross-information fusion (CIF) module to form a complementary structure for effectively modeling spatial-spectral features, thereby achieving the fusion and classification of multisource heterogeneous features. Experimental results on three benchmark datasets demonstrate that Slice Mamba outperforms existing advanced methods in fusion classification performance and exhibits greater robustness when applied to multispectral datasets.

Hyperspectral image (HSI) classification is fundamental to numerous remote sensing applications, enabling detailed analysis of material properties and environmental conditions. Recent Mamba built upon selective state space models (S6) have demonstrated exceptional advantages in long-range sequence modeling with linear computational efficiency, while Transformer based on self-attention mechanisms is particularly adept at capturing short-range dependencies. To leverage the complementary strengths of these models, this paper introduces a novel hybrid Mamba-Transformer framework (HSI-MFormer), effectively exploring the multiscale properties of hyperspectral data for HSI classification. Initially, a Multiscale Token Generation module (MTG) is developed, which converts the HSI cube into multiple spatial-spectral token groups across different scales. To adequately capture fine-grained multiscale spatial-spectral patterns, an Inner-scale Transformer Expert (ITE) is designed, which incorporates grouped self-attention operations to perform short-range sequence modeling within token groups at each scale. Meanwhile, a Cross-scale Mamba Expert (CME) is introduced, which integrates a cross-scale serialization mechanism and bidirectional Mamba block for long-range sequence modeling, further exploring the interactions and complementarity between token groups across different scales. Several hybrid strategies for integrating the ITE and CME are investigated to maximize their complementarity, including parallel, interval, and serial structures. Extensive experiments demonstrate that the propsed HSI-MFormer significantly out-performs the state-of-the-art Transformer-based and Mamba-based HSI classification methods. The code is available at https://github.com/tubingnuist/HSI-MFormer.

Traditional supervised deep learning (DL) methods for hyperspectral image (HSI) classification are severely limited by the quality and quantity of labels. Furthermore, existing feature extraction methods generally lack the fusion of multiscale feature information, struggling to handle complex scenarios. To counter these problems, this work investigates a feature extraction module based on self-supervised graph masked autoencoders (SGMAEs). It innovatively employs graph masked autoencoders to achieve self-supervised label-free feature extraction for the complete set of samples, utilizing a multiscale graph convolutional network encoder (MGCNE) and cross correlation decoder (CCD) to extract and fuse multiscale spatial-spectral features of HSI data, respectively. Specifically, the HSI data is first converted into an edge-masked perturbed graph to label-freely extract multiscale feature representations of all pixel samples, and then fed into the MGCNE to obtain multilayer feature vectors for the pixel nodes. To reconstruct the masked edges for the fusion of multiscale features, the CCD applies cross correlation calculations to the nodes of the true edges at the masked positions and the fake edges at the random positions. The contrastive learning loss function is proposed for training of the autoencoder, which calculates the loss for the existence estimates of edges generated by cross correlation calculations. The pretrained MGCNE possesses an efficient self-supervised multiscale spatial-spectral feature extraction capability, along with strong generalizability, which significantly improves the accuracy of various mainstream models in downstream classification tasks. Extensive experiments and analyses on multiple HSI datasets demonstrate that our proposed SGMAE significantly enhances the model performance of various supervised classifiers and achieves superior performance in comparison to mainstream models.

Hyperspectral image (HSI) classification is crucial for remote sensing research, while its high-dimensional features make traditional algorithms difficult to cope with. Despite the breakthroughs in deep learning, the high computational complexity and energy consumption limit its application in resource-limited environments. Spiking neural networks (SNNs), mimicking the brain’s information processing with low power consumption, have emerged as a promising alternative for edge computing. However, SNNs struggle with complex tasks due to the nondifferentiability of spike signals, which complicates training and exhibits limitations in extracting deep features and modeling long-range dependencies. In this article, we propose a novel SNN framework that addresses these challenges by enhancing feature extraction and efficiently capturing dependencies in hyperspectral data. Our framework integrates an adaptive refocusing convolutional layer with a spike self-attention (SSA) mechanism. The adaptive refocusing convolutional layer employs learnable parameters to dynamically adjust the convolutional kernel’s response to input spike data, improving feature representation. The adaptive refocusing convolutional layer uses learnable parameters to dynamically adjust kernel responses to input spike data, enhancing feature representation. Experimental results show that this model achieves over 96% classification accuracy in a single time step, significantly surpassing current methods and effectively solving the problem of low accuracy at short time steps in SNNs. Additionally, this framework reduces computational energy consumption by approximately$12.5\times $compared to similar, offering new potential for edge intelligence applications.

With the development of infrared technology, infrared small target detection (IRSTD) is widely applied in fields such as environmental monitoring, marine rescue, and forest fire prevention. Existing IRSTD methods are often based on spatial domain approaches, which preserve target features in the spatial domain but overlook the characteristics of infrared small targets in the frequency domain. In frequency domain methods, infrared small targets are typically considered as high-frequency components, while the continuous background is regarded as low-frequency components. However, infrared small targets often have complex backgrounds, strong edges, and noise generated during imaging, all of which are also reflected as high-frequency components, leading to false detections. To overcome this issue and fully explore the potential of IRSTD in the frequency domain, we propose a novel network, SFDTNet, which integrates frequency-domain attention and U-Structure for IRSTD. In the encoding phase, spatial feature extraction is applied to the infrared small target. In the decoding stage, global-scale spatial features are modeled in the frequency domain to achieve more precise reconstruction of small targets while reducing the interference of background high-frequency clutter. Frequency domain self-attention (FDSA) introduces an attention mechanism to model global information in the frequency domain and capture the importance of different frequency components. Adaptive frequency selection network (AFSN) incorporates learnable masks to adaptively modulate high- and low-frequency components in the frequency domain. Finally, a deep supervision strategy is employed to help the network learn features more effectively. Experimental results demonstrate that it effectively retains the shape and contours of small targets while achieving a very low false detection rate. Compared with existing state-of-the-art methods, our approach shows superior performance and better robustness.

Accurately obtaining the optical/microphysical characteristics of aerosols from satellite data is important for environmental protection and air quality monitoring. In this study, using Sentinel-2 satellite data, we propose a high-resolution satellite aerosol retrieval algorithm (CKAN) via convolutional neural network (CNN) and Kolmogorov-Arnold network (KAN). Unlike traditional retrieval algorithms that require the construction of physical models, the CKAN algorithm relies entirely on deep learning. This algorithm focuses on extracting high-dimensional information from the data through CNN and learning the potential nonlinear relationships between the data through the powerful fitting ability of KAN. Compared with the existing algorithms, the CKAN algorithm is characterized by simplicity and accuracy, and does not require a large amount of auxiliary meteorological data (e.g., relative humidity and ground air pressure) enables retrieval of various aerosol parameters, including Aerosol Optical Depth (AOD), Fine-mode AOD (FAOD), Coarse-mode AOD (CAOD), and Single Scattering Albedo (SSA). To demonstrate the effectiveness of the algorithm, we retrieved aerosol optical/microphysical characteristics from Sentinel-2 imagery for four study areas. Results indicate that both the AOD and FAOD retrieved by the CKAN algorithm exhibit a high degree of correlation ( R > 0.90) with AERONET products. For CAOD and SSA, although a few poor retrievals resulted in a low overall correlation, CKAN’s retrievals show good agreement with AERONET products. The CKAN algorithm has good accuracy in high-resolution satellite aerosol retrievals, and is expected to be widely used in urban-scale aerosol monitoring.

Graph convolutional network (GCN) has garnered significant attention in hyperspectral image (HSI) classification due to their ability to model non-Euclidean structured data. Compared with convolutional neural network (CNN), GCN can perform convolutions over irregular image regions and learn global dependencies among pixels in the whole image. Most existing GCN-based methods in the HSI community rely on average aggregation or weighted average aggregation strategies to aggregate neighboring node features. This process tends to obscure the differences between the nodes. However, for HSI classification tasks with obvious intra-class variability, average aggregation is a suboptimal choice. Moreover, the quality of the initial graph structure plays a crucial role in the model’s capacity to represent spectral relationships effectively. To mitigate these issues, we propose a neighborhood adaptive graph isomorphism network (NAGIN) for HSI classification to ensure that the diversified spectra representation of land-cover can be effectively captured. The neighborhood adaptive block (NAB) enhances spectral discriminability between land-cover classes via spectral reconstruction, enabling more precise removal of anomalous pixels in neighboring nodes. The graph isomorphism network (GIN) aggregates the features of neighboring nodes in an isomorphic manner to obtain multiple spectral expressions of the same type of land-cover, ensuring that the spectral features of different land-cover classes can be accurately distinguished. The Kolmogorov-Arnold network (KAN) leverages its ability to learn adaptive activation functions to better extract and refine the spectral features aggregated by GIN. Experimental results demonstrate that NAB can effectively improve the quality of the graph structure, the GIN aggregation method is competitive in HSI classification, and the proposed NAGIN outperforms the state-of-the-art methods on several public HSI datasets.

Hyperspectral image anomaly detection faces the challenge of difficulty in annotating anomalous targets. Autoencoder(AE)-based methods are widely used due to their excellent image reconstruction capability. However, traditional grid-based image representation methods struggle to capture long-range dependencies and model non-Euclidean structures. To address these issues, this paper proposes a self-supervised Masked Graph AutoEncoder (MGAE) for hyperspectral anomaly detection. MGAE utilizes a Graph Attention Network (GAT) autoencoder to reconstruct the background of hyperspectral images and identifies anomalies by comparing the reconstructed features with the original features. Specifically, we constructs a topological graph structure of the hyperspectral image, which is then input into the GAT autoencoder for reconstruction, leveraging the multi-head attention mechanism to learn spatial and spectral features. To prevent the decoder from learning trivial solutions, we introduce a re-masking strategy that randomly masks both the input features and hidden representations during training, forcing the model to learn and reconstruct features under limited information, thereby improving detection performance. Additionally, the proposed loss function with graph Laplacian regularization (Twice Loss) minimizes variations in feature representations, leading to more consistent background reconstruction. Experimental results on several real-world hyperspectral datasets demonstrate that MGAE outperforms existing methods.

Autoencoders (AEs) have received extensive attention in hyperspectral anomaly detection (HAD) due to their capability to separate the background from the anomaly based on the reconstruction error. However, the existing AE methods routinely fail to adequately exploit spatial information and may precisely reconstruct anomalies, thereby affecting the detection accuracy. To address these issues, this study proposes a novel Multi-scale Autoencoder Suppression Strategy (MASS). The underlying principle of MASS is to prioritize the reconstruction of background information over anomalies. In the encoding stage, the Local Feature Extractor, which integrates Convolution and Omni-Dimensional Dynamic Convolution (ODConv), is combined with the Global Feature Extractor based on Transformer to effectively extract multi-scale features. Furthermore, a Self-Attention Suppression module (SAS) is devised to diminish the influence of anomalous pixels, enabling the network to focus more intently on the precise reconstruction of the background. During the process of network learning, a mask derived from the test outcomes of each iteration is integrated into the loss function computation, encompassing only the positions with low anomaly scores from the preceding detection round. Experiments on eight datasets demonstrate that the proposed method is significantly superior to several traditional methods and deep learning methods in terms of performance.

Hyperspectral image (HSI) classification has garnered substantial attention in remote sensing fields. Recent mamba architectures built upon the selective state-space models (S6) have demonstrated enormous potential in long-range sequence modeling. However, the high dimensionality of hyperspectral data and information redundancy pose challenges to the application of S6 in HSI classification, suffering from suboptimal performance and computational efficiency. In light of this, this article investigates a lightweight interval group spatial-spectral mamba framework (IGroupSS-Mamba) for HSI classification, which allows for multidirectional and multiscale global spatial-spectral information extraction in a grouping and hierarchical manner. Technically, an interval group S6 mechanism (IGSM) is developed as the core component, which partitions high-dimensional features into multiple nonoverlapping groups at intervals, and then integrates a unidirectional S6 for each group with a specific scanning direction to achieve nonredundant sequence modeling. Compared with conventional applying multidirectional scanning to all bands, this grouping strategy leverages the complementary strengths of different scanning directions while decreasing computational costs. To adequately capture the spatial-spectral contextual information, an interval group spatial-spectral block (IGSSB) is introduced, in which two IGSM-based spatial and spectral operators are cascaded to characterize the global spatial-spectral relationship along the spatial and spectral dimensions, respectively. IGroupSS-Mamba is constructed as a hierarchical structure stacked by multiple IGSSB blocks, integrating a pixel aggregation-based downsampling strategy for multiscale spatial-spectral semantic learning from shallow to deep stages. Extensive experiments demonstrate that IGroupSS-Mamba significantly outperforms the state-of-the-art methods in classification accuracy and achieves lower model parameters and floating point operations (FLOPs). The code is available athttps://github.com/IIP-Team/IGroupSS-Mamba.

Hyperspectral image (HSI) classification constitutes a significant foundation for remote sensing analysis. Transformer architecture establishes long-range dependencies with a self-attention mechanism (SA), which exhibits advantages in HSI classification. However, most existing transformer-based methods are inadequate in exploring the multiscale properties of hybrid spatial and spectral information inherent in HSI data. To countermeasure this problem, this work investigates a hybrid multiscale spatial–spectral framework (HMSSF). It innovatively models global dependencies across multiple scales from both spatial and spectral domains, which allows for cooperatively capturing hybrid multiscale spatial and spectral characteristics for HSI classification. Technically, a spatial–spectral token generation (SSTG) module is first designed to generate the spatial tokens and spectral tokens. Then, a multiscale SA (MSSA) is developed to achieve multiscale attention modeling by constructing different dimensional attention heads per attention layer. This mechanism is adaptively integrated into both spatial and spectral branches for hybrid multiscale feature extraction. Furthermore, a spatial–spectral attention aggregation (SSAA) module is introduced to dynamically fuse the multiscale spatial and spectral features to enhance the classification robustness. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods on several public HSI datasets.

Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional neural networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies. However, these architectures suffer from limited receptive fields and quadratic computational complexity, respectively. Fortunately, recent Mamba architectures built upon the state space models (SSMs) integrate the advantages of long-range sequence modeling and linear computational efficiency, exhibiting substantial potential in low-dimensional scenarios. Motivated by this, we propose a novel 3D-spectral-spatial mamba (3DSS-Mamba) framework for HSI classification, allowing for global spectral-spatial relationship modeling with greater computational efficiency. Technically, a spectral-spatial token generation (SSTG) module is designed to convert the HSI cube into a set of 3-D spectral-spatial tokens. To overcome the limitations of traditional Mamba, which is confined to modeling causal sequences and inadaptable to high-dimensional scenarios, a 3D-spectral-spatial selective scanning (3DSS) mechanism is introduced, which performs pixel-wise selective scanning on 3-D hyperspectral tokens along the spectral and spatial dimensions. Five scanning routes are constructed to investigate the impact of dimension prioritization. The 3DSS scanning mechanism combined with conventional mapping operations forms the 3D-spectral-spatial mamba block (3DMB), enabling the extraction of global spectral-spatial semantic representations. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods on HSI classification benchmarks. The code is available athttps://github.com/IIP-Team/3DSS-Mamba.

Since the 1960s, remote sensing (as an innovative, comprehensive, and interdisciplinary academic area) has been adopted in a wide range of disciplines related to Earth observation, including hydrology, ecology, oceanography, glaciology, geology, military, intelligence, business, economy, and planning [1]-[3]. The constant development of the remote sensing image acquisition technology now allows for the collection of a wide variety of images with different characteristics and resolutions, obtained by remote sensing instruments mounted on spacecraft or aircraft platforms. These images record some type of signal or energy measured from the Earth's surface, which depends on the type of sensor used.

Remote sensing has evolved into a multidisciplinary field involving many different areas such as sensor technology, computing, and advanced applications. Information extraction now plays a fundamental role in the exploitation of the massive amount of data collected by earth observation instruments. In this Point of View, the authors analyze the evolution of this field, identifying three main phases in its development. The first period, which was marked by advances in digital signal processing, led to a significant development of statistical processing methods. The second phase was based on advances in physical models and brought an era of quantitative remote sensing which lasted until the first decade of this century. In the third and current period, information extraction techniques are gradually adopting advanced artificial intelligence models in an effort to cope with the tremendous increase in data volume. This article describes some of these recent advances and addresses challenges caused by the 4Vs (volume, velocity, variety, and veracity) of big data. Finally, the authors offer insight into future directions in this multidisciplinary field.

Karst rocky desertification is a special kind of land desertification developed under violent human impacts on the vulnerable eco-geo-environment of karst ecosystem. The fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil and exposed bedrock are key indicators of the extent and degree of land degradation in karst region. The vegetation fractional cover can be estimated approximately from remote sensing with vegetation indices. However, the vegetation indices cannot be easily applicable to all land cover types. In this study, we developed new spectral indices, karst rocky desertification synthesis indices (KRDSI), were then designed based on tied-spectrum permutation and unique spectral characteristics of main land cover types. Comparing with the use of traditional vegetation indices and LSU, the KRDSI was more consistent with the field measurement of main land cover fractions. Our study indicates that KRDSI is a useful tool for karst rocky desertification monitoring with remotely sensed data.