In recent years, the utilization of hyperspectral image (HSI) and light detection and ranging (LiDAR) for collaborative classification has emerged as a significant research direction in earth observation tasks, with diverse joint classification algorithms showing promising performance using varying network architectures. However, these methodologies infrequently address the challenge of fusion arising from the substantially larger volume of HSI feature information compared to LiDAR features. Moreover, the effective learning of HSI and LiDAR features while mitigating modality conflicts remains an area that necessitates further investigation. As such, a Fractional Fourier Enhanced Fusion Network based on Pareto Optimization (FrFENet) is proposed for HSI and LiDAR Data classification. To address the disparity in information volume between modalities, a weighted fractional Fourier enhanced fusion module (WFrFEF) is introduced, which applies a weighted fractional Fourier transform to HSI features, enhancing their representations and facilitating balanced fusion with LiDAR features. Furthermore, a Pareto-based soft optimization strategy, HLPareto, is designed to balance learning rates across HSI and LiDAR features in a dual-branch network, effectively avoiding optimization conflicts. Additionally, a spatial-spectral integration module (SSIM) and an elevation information enhancement module (EIEM) are developed to improve feature extraction. The SSIM enables effective spatial-spectral fusion by facilitating token-level interactions, while the EIEM enhances elevation feature representation, preserving spatial geometric information in LiDAR data. Extensive experiments and comparative analyses conducted on three widely utilized HSI and LiDAR datasets have shown that the proposed FrFENet exhibits superior classification 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.

Hyperspectral imagery (HSI) delivers detailed spectral information critical for remote sensing applications such as high-accuracy land cover classification, quantitative parameter retrieval, and environmental monitoring. However, satellite-borne HSI often suffers from limited spatial resolution owing to inherent sensor constraints, whereas airborne HSI is constrained by restricted spatial coverage. Reconstructing high-resolution HSI from multispectral imagery emerges as a promising strategy to address these challenges. In this study, we propose the Frequency-Enhanced Spatial-Spectral Network (FESSN), a computationally efficient architecture that innovatively integrates multi-domain fusion across spatial, spectral, and frequency domains to achieve superior reconstruction performance. A key innovation is the neural network-driven frequency enhanced modulation (FEM), which adaptively refines spectral amplitudes and phases via fast Fourier transform, providing interpretable, parameter-efficient enhancements to bridge spatial-spectral modeling gaps. Meanwhile, a Mamba-based Multi-Scale Spatial Fusion module (MMSAF) that seamlessly integrates local features with long-range dependencies, and a U-shaped Spectral Module (USEM) that integrates Mamba and attention mechanisms to model inter-group and intra-group relationships, while adhering to spectral sparsity priors. The experimental results demonstrate FESSN outperforming six state-of-the-art methods in metrics like RMSE (up to 5.31% improvement), PSNR, SAM, ERGAS, and SSIM. Downstream tasks in land cover classification further validate its utility, positioning FESSN as a breakthrough in efficient, high-fidelity HSI reconstruction. To facilitate reproducibility and further research, the code will be publicly available at https://github.com/KustAIRS/TGRS-FESSN.

Change detection (CD) in remote sensing imagery is identified as a pivotal task in the field of Earth observation, while it usually confronts the dilemma of intricate data and minor alterations. To address the stated challenge, this letter presents an innovative frequency-temporal attention network for CD (FTAN), which incorporates two advanced modules including the multidimensional convolutional frequency attention module (MCFA) and the interactive attention module (IAM). Specifically, the MCFA module is essential for enhancing sensitivity in CD by merging multiscale spatial and frequency domain features. As a supplement to MCFA, the IAM aggregates category-related tokens and processes cross-attention information from different time phases. The seamless integration of MCFA and IAM empowers the FTAN network with enhanced capabilities to detect minor regions and edges accurately. Experiments on datasets like LEVIR-CD and DSIFN-CD demonstrate superior performance by outperforming existing models in F1 scores and IoU metrics. Our code and pretrained models will be released athttps://github.com/chirsycy/FTAN.

Coral reef is a typical marine ecosystem and has significant implications for protecting marine biodiversity, and maintaining marine ecological balance. Accurate geomorphic information is the base of coral reef conservation, which usually is extracted by high-resolution remote sensing. Recent classification methods for coral reef geomorphology always focus on the extraction of deep spectral and texture features, ignoring the inherent geospatial information of geomorphology and losing the shallow-layer information, which leads to low classification accuracy. This paper proposes a deep learning classification method for coral reef geomorphology, named as GCU-Net which integrates the convolutional attention mechanism and the geo-spatial cognition. Experiments were carried out in North Reef and Zhaoshu Island geomorphology with the Gaofen-2 (GF-2) satellite image. The results demonstrate that the GCU-Net’s accurate classification with an overall accuracy (OA) of 90.46% and 88.92%, respectively, which better extracts the useful information in the shallow-layer, and effectively reduces the omission and misclassification of geomorphic types due to the different spatial positions. Our method exhibits excellent classification performance with an OA improvement of over 7% compared to the comparison method. Therefore, the method proposed in this paper is more effective in obtaining accurate information on coral reef geomorphology and can provide technical support for carrying out large-scale fine monitoring of coral reefs.

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.

Oriented to adaptive recognition of the new land-cover categories, incremental classification (IC) that aims to complete adaptive classification with continuous learning is urgent and crucial for hyperspectral image classification (HSIC). Nevertheless, deep-learning-based HSIC models adopted the learning paradigm with fixed classes yield unsatisfactory inference in the situation of IC due to the catastrophic forgetting problem. To eliminate the recognition gap and maintain the old knowledge during IC, in this paper, we propose a novel approach called the distillation-constrained prototype representation network (DCPRN) for hyperspectral image incremental classification (HSIIC). The primary goal of DCPRN is to enhance the discriminative capability for recognizing the original classes in HSIIC, while effectively integrating both the original and incremental knowledge to facilitate adaptive learning. Specifically, the proposed framework incorporates a prototype representation mechanism, which serves as a bridge for knowledge transfer and integration between the initial and incremental learning phases of HSIIC. Additionally, we present a dual knowledge distillation module in incremental learning, which integrates discriminative information at both the feature and decision level. In this way, the proposed mechanism enables flexible and dynamic adaptation to new classes and overcomes the limitations of fixed-category feature learning. Extensive experimental analysis conducted on three popular data sets validates the superiority of the proposed DCPRN method compared with other typical HSIIC approaches.

Recently, marine oil spill incidents occur frequently, causing serious pollution, which has seriously endangered marine ecological environment security. The type of oil spill pollution is related to the formulation of punishment and cleaning scheme, which is an important basis for the disposal of oil spill pollution. Hyperspectral remote sensing is an effective means to monitor marine oil spills. Different types of light oils are difficult to identify effectively, which can not meet the needs of accurate monitoring applications. In this paper, the outdoor oil spill experiment is implemented. The data of five typical oil products are obtained by unmanned airborne hyperspectral imager, and the feature extraction and analysis are carried out. The RPnet deep learning recognition model of oil types under multi feature fusion is constructed to realize the effective identification of different oil spill types. It can provide important technical support for offshore oil spill monitoring of relevant business departments.

Ocean oil spill is an emergency with great harm. Optical remote sensing is an important means to monitor oil spill on the sea surface. Due to the influence of cloud and weather and the limitation of satellite revisit period, only limited sample data can be obtained. In the case of limited samples, the ability of learning sample features using a single supervised classifier is limited, which can not meet the needs of accurately monitor oil spill. This paper takes GF-1 WFV oil spill image as data source, and uses four classical supervised classification algorithms to extract oil spill information. From the point of view of target recognition information fusion, the advantages of multiple supervised classification algorithms are integrated. Decision fusion algorithm is used to realize multi-source oil spill information fusion, so as to improve the detection accuracy of remote sensing oil spill.

The phenomenon of spectral aliasing exists for coastal wetland object types, which leads to class mixing. This letter proposes a multiobject convolutional neural network (CNN) decision fusion classification method for hyperspectral images of coastal wetlands. This method adopts decision fusion based on fuzzy membership rules applied to single-object CNN classification to obtain higher classification accuracy. Experimental results demonstrate the effectiveness of the proposed method for the six object types, including water, tidal flat, reed, and other vegetation types. The overall accuracy of the decision fusion classification method based on fuzzy membership is 82.11%, which is 3.33% and 6.24% higher than those of single-object feature band CNN and support vector machine methods. The classification method based on multiobject CNN decision fusion inherits the characteristics of single-object feature bands of the CNN, making it a practical approach to image classification under the challenging conditions in which class mixing occurs.