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.

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.

Remote sensing data from real-world scenarios manifests a long-tailed distribution, with the continuous emergence of new classes over time. Nevertheless, the existing class-incremental remote sensing classification models neglect the above long-tailed distribution phenomenon, which seriously damages their overall superior performance. Meanwhile, long-tail class-incremental learning developed in other areas focuses only on the classifier decision boundary optimization of the tail-class, while neglecting the robustness of the feature backbone. The feature backbone trained on the base classes causes a serious significant distribution shift for the incremental classes owing to the distributional differences between base and incremental classes. To solve these issues, we propose a continually evolved feature and classifiers learning (CEF-CL) framework for long-tail class-incremental remote sensing scene classification. Specifically, tail-class data are scaled and grafted onto head-class data to diversify the semantic information of the tail-class leveraging the rich context of the head classes, which can improve the generalization of the feature backbone. And then, an adaptive multi-scale feature fusion (AMFF) module is proposed to couple feature maps of head and tail classes scale by scale for generating virtual tail-class features that deeply perceive head-class information, which can further enhance the reliability of classifier decision boundary optimization. Furthermore, examples from old classes are regarded as pseudo-tail classes to participate in incremental learning, which greatly alleviates catastrophic forgetting of old classes. Extensive experiments on two remote sensing benchmarks demonstrate the superiority of the proposed CEF-CL in comparison with existing class-incremental learning.

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.

The joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data has attracted considerable attention in the field of remote sensing. Integrating the advantages of the two data sources can provide precise data support and analytical decision-making for remote-sensing applications. However, due to the inherent differences in properties and semantic information from heterogeneous data, most existing deep-learning methods suboptimally extract the characteristic features of both data sources while utilizing their interactive information. In this letter, we propose a dual-branch feature fusion network-based cross-modal enhanced CNN and Transformer (DF2NCECT) to make full use of the respective features and interactive information of multisource data. DF2NCECT consists of two main stages. One is the basic feature extraction stage, which builds a hybrid convolution module based on 3DCNN and inception structure to fully extract the joint features of HSI from multiple spatial perspectives. The other is the deep feature fusion stage, where the CNN and Transformer are designed in parallel to fully explore and fuse deep features between HSI and LiDAR. More importantly, to achieve efficacious interactive information between HSI and LiDAR, a cross-modal enhanced CNN and Transformer module (CECT) is designed to deeply enhance the fused interactive features from global/local perspectives. Experiments show that the proposed method is superior and outperforms the comparison methods by an average of 3.06% in OA on Houston2013 and 1.79% on Summer, respectively.

Convolutional neural network (CNN)-based hyperspectral image (HSI) classification models often exhibit high volume and complexity. This not only poses challenges in deploying them on mobile and embedded devices due to storage and power constraints but also introduces a dilemma between the growing demand for labeled samples and the high cost associated with manual labeling. To address these challenges, we propose an ultra-lightweight hybrid CNN based on redundancy removal (ULite-R2HCN), specifically designed for HSI classification in scenarios with limited samples. To reduce computational costs and enhance feature extraction effectiveness, we focus on optimizing the widely used depthwise convolution (DW-Conv) and pointwise convolution (PW-Conv) in the lightweight HSI classification model. For DW-Conv, we design a spatial convolution with redundancy removal (R2Spatial-Conv). This involves the design of multi-scale 3D convolution kernels with specific structures instead of 2D convolution kernels, aiming to reduce redundant convolution kernels and extract multi-scale spatial features. Simultaneously, for PW-Conv, we design a spectral convolution with redundancy removal (R2Spectral-Conv). This utilizes a “copy-splicing-grouping” structure to extract spectral features within arbitrary range intervals, effectively reducing redundant spectral extractions and capturing long-range spectral relationships. Numerous experiments have shown that the proposed ULite-R2HCN achieves higher classification accuracy with an ultra-light volume for a few training samples. In addition, sufficient ablation experiments also verified the advanced performance of the designed R2Spatial-Conv and R2Spectral-Conv.

Recent studies have demonstrated the potential of hybrid convolutional models that combine 3D and 2D convolutional neural networks (CNNs) for hyperspectral image (HSI) classification. However, these models do not fully utilize the benefits of hybrid convolution due to inefficient connections between the two types of CNNs. Moreover, most CNNs, including hybrid models, require a significant number of parameters and computational resources for accurate classification, which increases the need for labeled samples and computational cost. Although the common lightweight strategies like depthwise separable convolution (DSC) can reduce parameters and computation compared to normal convolution (NC), they often compromise accuracy. To address these challenges, we propose a lightweight hybrid convolutional neural network (Lite-HCNet) for HSI classification with minimal model parameters and computational effort. Firstly, we design a novel channel attention module (NCAM) and combine it with a convolutional kernel decomposition (CKD) strategy to propose a lightweight and efficient DSC (LE-DSC) deployed in Lite-HCNet. The LE-DSC not only reduces the DSC volume further but also enhances its performance. Secondly, a lightweight and efficient hybrid convolutional layer (LE-HCL) is designed in Lite-HCNet to explore the efficient connection structure between 3D CNNs and 2D CNNs. Experiments show that the Lite-HCNet reduces the required computational cost and practical deployment difficulty while offering advanced performance with a small number of training samples. Furthermore, abundant ablation experiments confirm the superior performance of the designed LE-DSC.

The semantic segmentation of multi-temporal remote sensing images to construct wetland land surface coverage is the basis for the perception and dynamic modeling of geographic scenes. However, the segmentation of Spartina alterniflora (S.alterniflora) in remote sensing images on wetlands faces the problems such as low level for cooperative interpretation in multi-temporal images and high fragmentation in the distribution of S.alterniflora. To solve the issues, a multiple attention network (MARNet) based on transfer learning is proposed. The method is designed with a plug-and-play attention module to enhance the learning of vegetation features and improve the network’s ability to focus on small areas of S.alterniflora. At the same time, MARNet designs the transfer learning architecture from both inter-domain alignment and intra-domain adaptation perspectives,aligning the statistical distribution by using the maximum mean difference (MMD) between the source and target domains, and entropy minimization within the domain of the target domain to enhance the high confidence prediction of this domain. In addition, since the samples have a serious imbalance problem, redundant cutting and splicing steps are employed for the prediction results to prevent the poor edge prediction of some image blocks. Experimental results on three cross-year RSIs datasets demonstrate that the proposed MARNet performs significantly better than other networks and is able to extract S.alterniflora in wetlands more accurately.

Accurate remote sensing monitoring of wetland ground objects is of great significance for ecological protection. In this letter, a convolutional neural network based on feature intersecting learning (FIL-CNN) is designed for wetland classification using multisource remote sensing data. The multi-layer shift feature fusion (MSFF) and attention feature selection (AFS) modules are designed to extract the complementary merits. Specifically, the MSFF is applied to each feature extraction unit, and the asymmetric information fusion is achieved through the spatial position shift of grouped features. Thus, the diversified feature representation is achieved. In the prediction stage, the AFS is executed to explore the channel mutually exclusive relationship between multisource features, resulting in emphasizing the meaningful features and eliminating the unnecessary ones. The experimental results prove the effectiveness and generalization of the proposed FIL-CNN on the wetland datasets.

The monitoring of coastal wetlands is of great importance to the protection of marine and terrestrial ecosystems. However, due to the complex environment, severe vegetation mixture, and difficulty of access, it is impossible to accurately classify coastal wetlands and identify their species with traditional classifiers. Despite the integration of multisource remote sensing data for performance enhancement, there are still challenges with acquiring and exploiting the complementary merits from multisource data. In this article, the depthwise feature interaction network (DFINet) is proposed for wetland classification. A depthwise cross attention module is designed to extract self-correlation and cross correlation from multisource feature pairs. In this way, meaningful complementary information is emphasized for classification. DFINet is optimized by coordinating consistency loss, discrimination loss, and classification loss. Accordingly, DFINet reaches the standard solution-space under the regularity of loss functions, while the spatial consistency and feature discrimination are preserved. Comprehensive experimental results on two hyperspectral and multispectral wetland datasets demonstrate that the proposed DFINet outperforms other competitive methods in terms of overall accuracy.