Unsupervised Hyperspectral Band Selection via Hybrid Graph Convolutional Network

Deep learning methods, especially convolutional neural networks(CNN), have been widely used in hyperspectral image(HSI) classification. Recently, graph convolutional networks (GCN) have shown great potential in HSI classification problem. However, the existing GCN-based methods have several problems. First, the existing methods rely too much on the adjacency matrix, which cannot be changed during training. Furthermore, most of them can only use a single kind of feature, and fail to extract the spectral-spatial information from the HSI. Finally, for the existing GCN-based methods, it is difficult to achieve the same accuracy as the mature CNN methods. In this paper, we propose a spectral-spatial hypergraph convolutional neural network (S²HCN) for HSI classification. Compared with the existing GCN-based methods, S²HCN has the following advantages. Different from the adjacency matrix that is fixed during training of GCN, S²HCN can dynamically update the weight of the hyperedge during training, which reduces the reliance on prior information to a certain extent. In addition, S²HCN generates hyperedges from the spectral and spatial features independently, and adopts the incidence matrix composed of all hyperedges to replace the adjacency matrix in GCN. In this way, the spectral and spatial features can be better integrated. Finally, compared to a simple graph structure, the hypergraph structure can express the high-dimensional relationships in the data, which is beneficial to classification problems. Sufficient experiments on two popular HSI datasets have proved the effectiveness of S²HCN.

The application of graph convolutional networks (GCN) in hyperspectral image (HSI) classification has become a promising method, thanks to its flexible convolution operation in any irregular image region. For the classification of HSI, GCN can extract more superpixel-level features with a topological structure, in comparison to the traditional convolutional neural networks (CNNs) using fixed square kernels distilling pixel-level features. To fully leverage the different levels of features, this study proposes a novel deep network referred to as a CNN-combined graph residual network ( C 2 GRN), which integrates the multilevel graph residual module and spectral-spatial features continuous learning module. During the extraction of topology information using the former module, HSI pixels are divided into superpixels and served as input nodes of the module to reduce the computational complexity and obtain the multilevel spatial relevance between adjacent superpixels. Besides, for the latter module, the spectral-spatial features are learnt continuously, which could obtain the finer pixel-level features. Finally, the captured spectral-spatial features of different levels are concatenated. This strategy could not only adequately utilize the correlation and difference of adjacent spatial but also obtain the finer and more valuable spectral-spatial information, which makes a significant boost in the HSI classification. Additionally, the experiment results demonstrate the superiority and availability of the C 2 GRN on three benchmark datasets of HSI, compared with the state-of-the-art methods for the classification of HSI.

Recently, hybrid networks, combining graph convolutional networks (GCNs) and convolutional neural networks (CNNs) into a unified framework, have garnered significant attention in hyperspectral image (HSI) classification. However, existing hybrid networks have the following limitations: 1) Existing methods primarily utilize simple fusion strategies such as concatenation or direct addition, resulting in the ineffective utilization of advantageous features. 2) Traditional GCNs only consider the relationship between pairs of vertices, limiting their ability to capture complex high-order and long-range correlations. In this work, a novel differential feature fusion network (DF2Net) is proposed for HSI classification. Specifically, DF2Net utilizes two subnetworks to learn features at different abstraction levels: the spectral-spatial hypergraph convolutional network (S2HGCN) for capturing complex high-order and long-range correlations, and the spectral-spatial convolution network (S2CN) for pixel-level local information extraction. Subsequently, we introduce an advantageous feature differential enhancement fusion (AFDEF) module, in which mutual enhancement of advantageous features from different network structures is performed, thereby improving the classification robustness of different regions in HSI. The experiments on four HSI benchmark datasets demonstrate that our DF2Net exhibits superior advantages over state-of-the-art models, particularly when the training samples are limited.

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have made considerable advances in hyperspectral image (HSI) classification. However, most CNN-based methods learn features at a single-scale in HSI data, which may be insufficient for multi-scale feature extraction in complex data scenes. To learn the relations among samples in non-grid data, GCNs are employed and combined with CNNs to process HSIs. Nevertheless, most methods based on CNN-GCN may overlook the integration of pixel-wise spectral signatures. In this paper, we propose a pyramid cascaded convolutional neural network with graph convolution (PCCGC) for hyperspectral image classification. It mainly comprises CNN-based and GCN-based subnetworks. Specifically, in the CNN-based subnetwork, a pyramid residual cascaded module and a pyramid convolution cascaded module are employed to extract multiscale spectral and spatial features separately, which can enhance the robustness of the proposed model. Furthermore, an adaptive feature-weighted fusion strategy is utilized to adaptively fuse multiscale spectral and spatial features. In the GCN-based subnetwork, a band selection network (BSNet) is used to learn the spectral signatures in the HSI using nonlinear inter-band dependencies. Then, the spectral-enhanced GCN module is utilized to extract and enhance the important features in the spectral matrix. Subsequently, a mutual-cooperative attention mechanism is constructed to align the spectral signatures between BSNet-based matrix with the spectral-enhanced GCN-based matrix for spectral signature integration. Abundant experiments performed on four widely used real HSI datasets show that our model achieves higher classification accuracy than the fourteen other comparative methods, which shows the superior classification performance of PCCGC over the state-of-the-art methods.

Recently, there have been continuous advancements in hyperspectral image classification (HSIC) approaches based on deep learning mechanisms. Nonetheless, information redundancy, high complexity and ineffective descriptions remain primary obstacles to present HSIC networks. In order to overcome these issues, an optimization-enabled deep convolutional neural network (Deep CNN) for HSIC is proposed in this work. Here, band selection, feature extraction, as well as classification are important tasks in processing these images. The band selection is performed by utilizing the double exponential smoothing-artificial flora optimization (DES-AFO) algorithm, which is the combination of double exponential smoothing (DES) and artificial flora optimization (AFO) for HSIC. There is a requirement for extracting competent features from hyperspectral imaging (HSI) to enhance the classifier’s accuracy. Here, the feature extraction is done by empirical wavelet transform and a convolutional neural network (CNN). As the next process, HSIC takes place using the Deep CNN, and it is trained by DES-AFO. The wide experimental outcomes on HSI show that the proposed mechanism has noteworthy benefits estimated with the conventional mechanisms concerning accuracy and TPR. Here, analysis states that the proposed mechanism obtained better accuracy and TPR values of 0.930, as well as 0.965.

Although graph convolutional network (GCN) has achieved remarkable success in hyperspectral image (HSI) classification, most existing GCN-based approaches have failed to realize a deep network structure due to the oversmoothing problem. This problem largely limits the expression ability and feature extraction ability of GCN and hampers GCN's capacity to model long-range relationships between samples in hyperspectral (HS) scenes. Moreover, there is a lack of theoretical analysis in those works that constructed deep GCN for HSI classification to illustrate how they overcome the oversmoothing problem. Aside from this, the characteristics and complexity of HSI are often neglected when constructing deep GCN models in HSI classification. To address these problems, a novel deep graph network based on first-order smoothing is proposed for HSI classification. Specifically, a local and global topologically consistent graph is constructed to thoroughly explore the union between fine pixel information and semantic superpixel information. Subsequently, a novel propagation procedure is proposed to address the oversmoothing problem. We creatively build a residual connection to the first layer to emphasize the feature information aggregated from the first-order neighborhood, which adds node features that have not yet become indistinguishable into deep layer, and at the same time, it can be considered as a correction to the original pixels affected by spectral variation in the input graph. Finally, we demonstrate how first-order smoothing-based deep graph network (FSDGN) can slow down the convergence rate of the oversmoothing problem by analyzing the propagation of FSDGN from the standpoint of the Laplacian spectral domain. In addition, the results of experiments performed on three benchmark datasets demonstrate its superiority over other state-of-the-art methods.

Multiobjective band selection approach via an adaptive particle swarm optimizer for remote sensing hyperspectral images

Different crops, as well as the same crop at different growth stages, display distinct spectral and spatial characteristics in hyperspectral images (HSIs) due to variations in their chemical composition and structural features. However, the narrow bandwidth and closely spaced spectral channels of HSIs result in significant data redundancy, posing challenges to crop identification and classification. Therefore, the dimensionality reduction in HSIs is crucial. Band selection as a widely used method for reducing dimensionality has been extensively applied in research on crop identification and mapping. In this paper, a crop superpixel-based affinity propagation (CS-AP) band selection method is proposed for crop identification and mapping in agriculture using HSIs. The approach begins by gathering crop superpixels; then, a spectral band selection criterion is developed by analyzing the variations in the spectral and spatial characteristics of crop superpixels. Finally, crop identification bands are determined through an efficient clustering approach, AP. Two typical agricultural hyperspectral data sets, the Salinas Valley data set and the Indian Pines data set, are selected for validation, each containing 16 crop classes, respectively. The experimental results show that the proposed CS-AP method achieves a mapping accuracy of 92.4% for the Salinas Valley data set and 88.6% for the Indian Pines data set. When compared to using all bands, two unsupervised band selection techniques, and three semi-supervised band selection techniques, the proposed method outperforms others with an improvement of 3.1% and 4.3% for the Salinas Valley and Indian Pines data sets, respectively. Indicate that the proposed CS-AP method achieves superior mapping accuracy by selecting fewer bands with greater crop identification capability compared to the other band selection methods. This research’s significant results demonstrate the potential of this approach in precision agriculture, offering a more cost-effective and timely solution for large-scale crop mapping and monitoring in the future.

Band selection (BS) aims to choose a salient subset implied sufficient information from the numerous bands, which supplies a significantly efficient way to alleviate the barrier of dimensionality disaster for hyperspectral image classification (HSIC). This letter develops a semisupervised BS approach based on dual-constrained low-rank representation BS (DCLRR-BS) with two regularizations for HSIC. To be specific, a low-rank representation model is first proposed with super-pixel and imbalanced class-wise constraints, which are explicitly integrated to improve the performance of the band description. Next, the clusters are built adaptively based on graph theory in an unsupervised manner to rapid selection efficiency. A selection criterion is last designed to highlight the prominent band of each subset cluster to fulfill the BS procedure. Experimental results conducted on four types of classifiers with two real hyperspectral image (HSI) data sets demonstrate that the proposed DCLRR-BS method performs well in the imbalanced HSIC area.

Hyperspectral image (HSI) classification is fundamental to a wide range of remote sensing applications, such as precision agriculture, environmental monitoring, and urban planning, because HSIs provide rich spectral signatures that enable the discrimination of subtle material differences. Deep learning approaches, including Convolutional Neural Networks (CNNs), Graph Convolutional Networks (GCNs), and Transformers, have achieved strong performance in learning spatial–spectral representations. However, these models often face difficulties in jointly modeling long-range dependencies, fine-grained local structures, and non-Euclidean spatial relationships, particularly when labeled training data are scarce. This paper proposes a Spectral–Spatial Graph Transformer Network (SSGTN), a dual-branch architecture that integrates superpixel-based graph modeling with Transformer-based global reasoning. SSGTN consists of four key components, namely (1) an LDA-SLIC superpixel graph construction module that preserves discriminative spectral–spatial structures while reducing computational complexity, (2) a lightweight spectral denoising module based on 1×1 convolutions and batch normalization to suppress redundant and noisy bands, (3) a Spectral–Spatial Shift Module (SSSM) that enables efficient multi-scale feature fusion through channel-wise and spatial-wise shift operations, and (4) a dual-branch GCN-Transformer block that jointly models local graph topology and global spectral–spatial dependencies. Extensive experiments on three public HSI datasets (Indian Pines, WHU-Hi-LongKou, and Houston2018) under limited supervision (1% training samples) demonstrate that SSGTN consistently outperforms state-of-the-art CNN-, Transformer-, Mamba-, and GCN-based methods in overall accuracy, Average Accuracy, and the κ coefficient. The proposed framework provides an effective baseline for HSI classification under limited supervision and highlights the benefits of integrating graph-based structural priors with global contextual modeling.

Hyperspectral images (HSI) offer detailed spectral reflectance information about sensed objects through provision of information on hundreds of narrow spectral bands. HSI have a leading role in a broad range of applications, such as in forestry, agriculture, geology, and environmental sciences. The monitoring and management of agricultural lands is of great importance for meeting the nutritional and other needs of a rapidly and continuously increasing world population. In relation to this, classification of HSI is an effective way for creating land use and land cover maps quickly and accurately. In recent years, classification of HSI using convolutional neural networks (CNN), which is a sub-field of deep learning, has become a very popular research topic and several CNN architectures have been developed by researchers. The aim of this study was to investigate the classification performance of CNN model on agricultural HSI scenes. For this purpose, a 3D-2D CNN framework and a well-known support vector machine (SVM) model were compared using the Indian Pines and Salinas Scene datasets that contain crop and mixed vegetation classes. As a result of this study, it was confirmed that use of 3D-2D CNN offers superior performance for classifying agricultural HSI datasets.

Band selection for hyperspectral images is an effective technique to mitigate the curse of dimensionality. A variety of band selection methods have been suggested in the past. This paper presents a novel band prioritization based on impurity function (IF) for the band selection of hyperspectral images. The proposed IF band selection (IFBS) is incorporated with particle swarm optimization (PSO) band selection which has been developed to effectively group highly correlated bands of hyperspectral images into high corrected modules. It uses a particle swarm optimization scheme, which is a well-known method to solve the optimization problems, to develop an effective feature extraction algorithm for hyperspectral imagery. After PSO method is applied to the band reduction of hyperspectral images, the proposed IFBS is applied to enhance the efficiency of band selection. The propose method is evaluated by MODIS/ASTER airborne simulator (MASTER) for land cover classification during the Pacrim II campaign. The performance of IFBS is validated by the supervised k-nearest neighbor (KNN) classifier. Experimental results demonstrate that the proposed IFBS approach is an effective method for dimensionality reduction and feature extraction. Compared to other band selection methods, IFBS can effectively select the most significant bands for the image classification of hyperspectral images.

Geometric deep learning provides a principled and universal way for the integration of imaging and non-imaging modes in the medical field. Graph Convolutional Networks (GCNs) have been widely explored in a variety of problems, such as disease prediction, segmentation, and matrix completion. Using large, multi-modal data sets, graphs can capture the interaction of individual elements represented as nodes in the graphs. In particular, in medical applications, nodes can represent individuals (patients or healthy controls) in a potentially large population and are accompanied by a set of features, while the edges of the graph contain the associations between subjects in an intuitive way. This representation allows the inclusion of rich imaging and non-imaging information as well as individual subject characteristics in the task of disease classification. This article gives an overview of graph convolutional neural networks, focusing on the application of graph convolutional neural networks in disease prediction, and discusses the challenges and prospects of graph convolutional neural networks in disease prediction.

Exploring the optimization of communication strategies for animation films in the context of cross-cultural communication, this research integrates the Internet of Things (IoT) and convolutional networks. The research constructs a collaborative filtering (CF) movie recommendation model based on a graph convolutional neural network (GCN) and investigates its application in cross-cultural communication. The fusion of IoT and convolutional networks in movie communication is also analyzed, and the effectiveness of the proposed GCN-CF model is validated through comparative experiments. The results indicate that, compared to other models, the GCN-CF model achieves the lowest Root Mean Square Error (RMSE) on the MovieLens 100 K and MovieLens 1 M datasets, with values of 0.8762 and 0.8275, respectively. Compared to traditional models, the GCN-CF model exhibits significantly superior performance in terms of RMSE, with reductions ranging from 0.6 to 5.2%, highlighting its heightened detection accuracy and overall performance. Moreover, the performance of the GCN-CF model is enhanced after introducing attention mechanisms and auxiliary information on both datasets, showing an improvement of 0.4% compared to the scenario without these additions. This data demonstrates the effectiveness of attention mechanisms and auxiliary information. Finally, the research presents an animation film communication strategy based on IoT and convolutional networks, offering novel insights for film production and communication, along with positive implications for cultural exchange and the advancement of the global media industry.

Fuzzy graph convolutional network for hyperspectral image classification