The integration of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data provides complementary spectral and spatial information for remote sensing applications. While previous studies have explored the role of band selection and grouping in HSI classification, little attention has been given to how the spectral sequence—or band order—affects classification outcomes when fused with LiDAR. In this work, we systematically investigate the influence of band order on HSI-LiDAR fusion performance. Through extensive experiments, we demonstrate that band order significantly impacts classification accuracy, revealing a previously overlooked factor in fusionbased models. Motivated by this observation, we propose a novel fusion architecture that not only integrates HSI and LiDAR data but also learns from multiple band order configurations. The proposed method enhances feature representation by adaptively fusing different spectral sequences, leading to improved classification accuracy. Experimental results on the Houston 2013 and Trento datasets show that our approach outperforms state-of-the-art fusion models. Data and code are available at https://github.com/Judyxyang/HSLiNets.

Multimodal image fusion has recently garnered increasing interest in the field of remote sensing. By leveraging the complementary information in different modalities, the fused results may be more favorable in characterizing objects of interest, thereby increasing the chance of a more comprehensive and accurate perception of the scene. Unfortunately, most existing fusion methods tend to extract modality-specific features independently without considering intermodal alignment and complementarity, leading to a suboptimal fusion process. To address this issue, we propose a novel interactive generative adversarial network (IG-GAN), for the task of multimodal image fusion. IG-GAN comprises guided dual streams tailored for enhanced learning of details and content, as well as cross-modal consistency. Specifically, a details-guided interactive running-in module (GIR1) and a content-guided interactive running-in module (GIR2) are developed, with the stronger modality serving as guidance for detail richness or content integrity, and the weaker one assisting. To fully integrate multigranularity features from dual-modality, a hierarchical fusion and reconstruction branch is established. Specifically, a shallow interactive fusion (SIF) module followed by a multilevel interactive fusion (MIF) module is designed to aggregate multilevel local and long-range features. Concerning feature decoding and fused image generation, a high-level interactive fusion and reconstruction module (HRM) is further developed. In addition, to empower the fusion network to generate fused images with complete content, sharp edges, and high fidelity without supervision, a loss function facilitating the mutual game between the generator and two discriminators is also formulated. Comparative experiments with 14 state-of-the-art methods are conducted on three datasets. Qualitative and quantitative results indicate that IG-GAN exhibits obvious superiority in terms of both visual effect and quantitative metrics. Moreover, experiments on two RGB-IR object detection datasets are also conducted, which demonstrate that IG-GAN can enhance the accuracy of object detection by integrating complementary information from different modalities. The code will be available athttps://github.com/flower6top.

Sparse unmixing (SU) of hyperspectral image (HSI), as a semisupervised approach, aims to find the optimal subset of the spectral library known in advance to represent each pixel in HSI. However, most of the existing SU methods cannot take full advantage of spatial information and mixed noise in HSI. To this end, we propose a superpixel-based noise-robust SU method (SNRSU) in the presence of mixed noise. First, we perform superpixel segmentation (SS) on the first principal component of HSI to extract the homogeneous regions. Then, we unmix each superpixel based on sparse representation (SR) and low-rank representation (LRR) in the maximuma posterioriframework, which can make full use of the spatial–spectral information in HSI under complex mixed noise. A number of experiments on simulated and real HSI datasets confirm the superior performance of the proposed SNRSU both qualitatively and quantitatively.

Band selection is an effective means to alleviate the curse of dimensionality in hyperspectral data. Many methods select a compact and low redundant band subset, which is inadequate as it may degrade the classification performance. Instead, more emphasis shall be put on selecting representative bands. In this article, we propose a robust unsupervised band selection method to address this issue. Our method reveals bandwise representativeness based on the comprehensive interband neighborhood structure. It incorporates an interband neighborhood graph into a sparse self-contained regression model in order to provide a reasonable measure for bandwise representativeness. The derived coefficient matrix not only uncovers bandwise importance values but also is coherent to the generalized interband local neighborhood structure. For constructing the interband neighboring structural graph, an integrated multigraph model is employed to achieve better generalization performance. It combines the benefit of multiple graphs but is insusceptible to the defects of a single one. To enhance the reliability of this model, a joint trace minimum and nonnegative constraint is imposed on the coefficient matrix. Accordingly, a multigraph integrated embedding and robust self-contained regression model (MGRSR) is formulated. In addition, an iterative update algorithm is developed to solve the problem. Comparative experiments on three hyperspectral data sets illustrate that MGRSR is robust to various data and has superior performance compared with several state-of-the-art methods.

Convolutional neural networks (CNNs) have demonstrated outstanding performance on image classification. To classify the hyperspectral images (HSIs), existing CNN-based approaches commonly adopt the architecture using single or several fixed spatial windows as inputs. This kind of architecture may lose contextual information or incorporate heterogeneous information due to the neglect of various land-cover distributions in HSIs. To deal with this problem, a novel attention multibranch CNN method based on adaptive region search (RS-AMCNN) is proposed for HSI classification. In RS-AMCNN, sizes and locations of spatial windows are searched in the nonlocal candidate region adaptively according to sample-specific distribution. These flexible spatial windows are input into several branches of RS-AMCNN. In each branch, convolutional long short-term memories (ConvLSTMs) are merged into CNN from shallow to deep layers, which not only extracts joint spatial-spectral features, but also exploits complementary information among different layers. Then, a branch attention mechanism is devised to emphasize more discriminative branches and suppress less useful ones. It forces RS-AMCNN to extract multiscale and multicontextual attention features for classification. Finally, RS-AMCNN is optimized end-to-end by combining the losses from the ramose classifiers of different branches and the main classifier. Experiments carried on several benchmark HSI data sets demonstrate that RS-AMCNN provides promising classification performance, especially in edge preservation and region uniformity.

Hyperspectral band selection is of great value to alleviate the curse of dimensionality. For many band selection methods, however, the neglect of bandwise usefulness tends to result in the loss of valuable bands, but the retention of useless ones; consequently, this causes deterioration of the classification performance. In this sense, bandwise significance should be emphasized. To address this issue, this article proposes a manifold-preserving and weakly redundant (MPWR) unsupervised band selection method. In the method, a manifold-preserving band-importance metric is put forward to measure the bandwise essentiality. This ensures the retention of bands involving abundant intrinsic structures conductive to classification. Specifically, aimed at obtaining the presented band-importance metric, an attainment algorithm is presented, which mainly relies on the embedding learning and linear regression, followed by the introduction of multi-normalization combination. In addition, concerning the massive redundancy caused by the highly correlated bands, MPWR further establishes a constrained band-weight optimization model. Then, both bandwise manifold-preserving capability and intraband correlation are fully integrated into the band selection process. To solve the problem, a corresponding algorithm within the framework of the alternating direction method of multipliers (ADMM) is also developed. Regarding evaluating the effectiveness of the proposed method, comparative experiments with the state-of-the-art methods are conducted on three public hyperspectral data sets. Experimental results demonstrate the superiority and robustness of MPWR.

Hyperspectral images (HSIs) have evident advantages in image understanding due to enormous spectral bands, and rich spatial information. Hundreds of spectral bands, however, actually play different roles in contributing to the class-specific classification. Then, treating each band equally may lead to the underuse or overuse of them. To address this issue, this paper introduces class-specific band contributions (BCs) into the spectral space, and proposes a weighted spectral-spatial classification method for HSIs. In the method, by incorporating BC characterized by F-measure into the distance-based posterior probability, a weighted spectral posterior probability (WSP) model is established. Furthermore, to exploit the spatial information, WSP is then combined with the spatial consistency constraint via an adaptive tradeoff parameter. Additionally, aimed at obtaining the class-dependent F-measures of each band, a semisupervised F-measure prediction method is also developed. Experiments on four hyperspectral data sets are conducted. Experimental results show the superiority of our proposed method over several state-of-the-art methods in terms of three widely used indexes.

Band selection is of great significance to alleviate the curse of dimensionality for hyperspectral (HSI) image application. In this letter, we propose a novel unsupervised band selection method for HSI classification. This method integrates both the overall accuracy and redundancy into the band selection process by formulating an optimization model. In the optimization problem, an adaptive balance parameter is designed to trade off the overall accuracy and redundancy. Additionally, we adopt an unsupervised overall accuracy prediction method to obtain the overall accuracy; thus, no ground truth or training samples is required. Experimental results on the ROSIS and RetigaEx data sets show that our method outperforms four representative methods in terms of classification accuracy and redundancy.

This paper proposes an image classification accuracy prediction based unsupervised band selection method for hyper spectral image classification. The key of this method is the prediction of overall classification accuracy for each spectral band with no ground truth or training samples. Under the hypothesis of Gaussian Mixture Model (GMM), we build the explicit expression between the overall accuracy and the distribution parameters of each class, which is denoted as the overall accuracy prediction equation (OCPE). Then, by employing the unsupervised mixture models learning algorithm to predict these distribution parameters, the overall accuracy is computable on the basis of the OCPE. Then, the candidate band subset is obtained by selecting the bands with relatively high overall accuracy. Finally, we use the divergence based band decor relation algorithm to further remove the redundant bands. Real hyper spectral images based experiments show that our band selection method is effective in comparison with other three well-known unsupervised band selection techniques.