Residual Group Channel and Space Attention Network for Hyperspectral Image Classification

In the last five years, deep learning has been introduced to tackle the hyperspectral image (HSI) classification and demonstrated good performance. In particular, the convolutional neural network (CNN)-based methods for HSI classification have made great progress. However, due to the high dimensionality of HSI and equal treatment of all bands, the performance of these methods is hampered by learning features from useless bands for classification. Moreover, for patchwise-based CNN models, equal treatment of spatial information from the pixel-centered neighborhood also hinders the performance of these methods. In this article, we propose an end-to-end residual spectral-spatial attention network (RSSAN) for HSI classification. The RSSAN takes raw 3-D cubes as input data without additional feature engineering. First, a spectral attention module is designed for spectral band selection from raw input data by emphasizing useful bands for classification and suppressing useless bands. Then, a spatial attention module is designed for the adaptive selection of spatial information by emphasizing pixels from the same class as the center pixel or those are useful for classification in the pixel-centered neighborhood and suppressing those from a different class or useless. Second, two attention modules are also used in the following CNN for adaptive feature refinement in spectral-spatial feature learning. Third, a sequential spectral-spatial attention module is embedded into a residual block to avoid overfitting and accelerate the training of the proposed model. Experimental studies demonstrate that the RSSAN achieved superior classification accuracy compared with the state of the art on three HSI data sets: Indian Pines (IN), University of Pavia (UP), and Kennedy Space Center (KSC).

In recent years, generative adversarial networks (GNAs), consisting of two competing 2D convolutional neural networks (CNNs) that are used as a generator and a discriminator, have shown their promising capabilities in hyperspectral image (HSI) classification tasks. Essentially, the performance of HSI classification lies in the feature extraction ability of both spectral and spatial information. The 3D CNN has excellent advantages in simultaneously mining the above two types of features but has rarely been used due to its high computational complexity. This paper proposes a hybrid spatial-spectral generative adversarial network (HSSGAN) for effective HSI classification. The hybrid CNN structure is developed for the construction of the generator and the discriminator. For the discriminator, the 3D CNN is utilized to extract the multi-band spatial-spectral feature, and then we use the 2D CNN to further represent the spatial information. To reduce the accuracy loss caused by information redundancy, a channel and spatial attention mechanism (CSAM) is specially designed. To be specific, a channel attention mechanism is exploited to enhance the discriminative spectral features. Furthermore, the spatial self-attention mechanism is developed to learn the long-term spatial similarity, which can effectively suppress invalid spatial features. Both quantitative and qualitative experiments implemented on four widely used hyperspectral datasets show that the proposed HSSGAN has a satisfactory classification effect compared to conventional methods, especially with few training samples.

Recently, graph convolutional network (GCN) has drawn increasing attention in hyperspectral image (HSI) classification, as it can process arbitrary non-Euclidean data. However, dynamic GCN that refines the graph heavily relies on the graph embedding in the previous layer, which will result in performance degradation when the embedding contains noise. In this letter, we propose a novel dual residual graph convolutional network (DRGCN) for HSI classification that integrates two adjacency matrices of dual GCN. In detail, one GCN applies a soft adjacency matrix to extract spatial features, the other utilizes the dynamic adjacency matrix to extract global context-aware features. Subsequently, the features extracted by dual GCN are fused to make full use of the complementary and correlated information among two graph representations. Moreover, we introduce residual learning to optimize graph convolutional layers during the training process, to alleviate the over-smoothing problem. The advantage of dual GCN is that it can extract robust and discriminative features from HSI. Extensive experiments on four HSI data sets, including Indian Pines, Pavia University, Salinas, and Houston University, demonstrate the effectiveness and superiority of our proposed DRGCN, even with small-sized training data.

Convolutional neural networks (CNNs) have achieved great results in hyperspectral image (HSI) classification in recent years. However, convolution kernels are reused among different spatial locations, known as spatial-agnostic or weight-sharing kernels. Furthermore, the preference of spatial compactness in convolution (typically, 3×3 kernel size) constrains the receptive field and the ability to capture long-range spatial interactions. To mitigate the above two issues, in this article, we combine a novel operation called involution with residual learning and develop a new deep residual involution network (DRIN) for HSI classification. The proposed DRIN could model long-range spatial interactions well by adopting enlarged involution kernels and realize feature learning in a fairly lightweight manner. Moreover, the vast and dynamic involution kernels are distinct over different spatial positions, which could prioritize the informative visual patterns in the spatial domain according to the spectral information of the target pixel. The proposed DRIN achieves better classification results when compared with both traditional machine learning-based and convolution-based methods on four HSI datasets. Especially in comparison with the convolutional baseline model, i.e., deep residual network (DRN), our involution-powered DRIN model increases the overall classification accuracy by 0.5%, 1.3%, 0.4%, and 2.3% on the University of Pavia, the University of Houston, the Salinas Valley, and the recently released HyRANK HSI benchmark datasets, respectively, demonstrating the potential of involution for HSI classification.

Deep learning models have obtained great success in hyperspectral image classification tasks. Nevertheless, they are usually vulnerable to adversarial attacks. Some existing works have been made to defend against adversarial attacks in HSI classification. These works primarily focus on lots of adversarial samples and spatial relationships while overlooking the strong long-range dependencies from HSI. To alleviate this problem, we propose a novel spectral spatial mamba adversarial defense network (SSMADNet) for hyperspectral adversarial image classification. It includes a dense involution branch, a spectral mamba branch, and a spatial multiscale mamba branch. The dense involution branch extracts embedding features via three dense involution layers. The spectral mamba branch can learn the spectral sequence information from HSI adversarial samples. The spatial multiscale mamba branch can model the long-range interaction of the whole image. Finally, a spectral spatial feature enhancement module is designed to adaptively enhance useful spectral spatial features of HSI. Extensive experimental results demonstrate that on five HSI adversarial datasets, the proposed SSMADNet achieves higher classification accuracies than state-of-the-art adversarial defense methods. In particular, our method obtains best OA (93.80%) on the Botswana adversarial data, which is much higher than the suboptimal method (OA = 90.30%).

Considering that the hyperspectral image (HSI) has a large number of spectrum bands, to optimize the features and make full use of more informative features, many papers have introduced attention mechanism to the models based on three-dimensional (3D) convolution. However, though the number of spectrum bands is large, there are many useless bands and noise, which may generate lots of useless features into the subsequent network and affect the learning efficiency of each convolutional layer. Therefore, how to reduce the influence of noise from HSI data itself and the classification process is key to the HSI classification tasks. In this paper, we proposed a 3D convolutional neural network (3D-CNN) based two-stage attention network (TSAN) for HSI classification. For one thing, the spectral-wise attention module in the first stage can optimize the whole spectrum by shielding useless spectrum bands and reducing the noise in the spectrum. For another, more discriminative spectral–spatial features are extracted and sent to the subsequent layers by channel-wise attention mechanism combined with soft thresholding in the second stage. In addition, we introduced non-local block to learn global spatial features and used a multi-scale network to combine the local space and the global space. The experiments carried out on three HSI datasets show that our proposed network for HSI classification tasks can indeed reduce the noise by soft thresholding and achieve promising classification performance.

Convolutional Neural Network (CNN) has developed hyperspectral image (HSI) classification effectively. Although many CNN-based models can extract local features in HSI, it is difficult for them to extract global features. With its ability to capture long-range dependencies, Transformer is gradually gaining prominence in HSI classification, but it may overlook some local details when extracting features. To address these issues, we proposed a CNN and transformer interaction network (CTIN) for HSI classification. Firstly, A dual-branch structure was constructed in which CNN and Transformer are arranged in parallel to simultaneously extract global features and local features in HSI. Secondly, a feature interaction module has been imported between the two branches, thus facilitating a bi-directional flow of information between the global and local feature spaces. In this way, the network structure combines the advantages of CNN and Transformer in extracting features as much as possible. In addition, a token generation method is designed to harness abundant contextual information that is relevant to the centre pixel, and improve the accuracy of the final classification. Experiments were conducted on four hyperspectral datasets (two classical datasets – Indian Pines, Salinas Valley, a new satellite dataset – Yellow River, and an self-made UAV dataset-Yellow River Willow). Experimental results show that the proposed method outperforms the other state-of-the-art methods, with overall accuracies of 99.21%, 99.61%, 92.40%, and 98.17%, respectively.

Recently, the convolutional neural network (CNN) has gained widespread adoption in the hyperspectral image (HSI) classification owing to its remarkable feature extraction capability. However, the fixed acceptance domain of CNN restricts it to Euclidean image data only, making it difficult to capture complex information in hyperspectral data. To overcome this problem, much attention has been paid to the graph attention network (GAT), which can effectively model graph structure and capture complex dependencies between nodes. However, GAT usually acts on superpixel nodes, which may lead to the loss of pixel-level information. To better integrate the advantages of both, we propose a CNN-assisted multi-hop graph attention network (CMGAT) for HSI classification. Specifically, a parallel dual-branch architecture is first constructed to simultaneously capture spectral-spatial features from hyperspectral data at the superpixel and pixel levels using GAT and CNN, respectively. On this basis, the multi-hop and multi-scale mechanisms are further employed to construct a multi-hop GAT module and a multi-scale CNN module to capture diverse feature information. Secondly, an attention module is cascaded before the multi-scale CNN module to improve classification performance. Eventually, the output information from the two branches is weighted and fused to produce the classification result. We performed experiments on four benchmark HSI datasets, including Indian Pines (IP), University of Pavia (UP), Salinas Valley (SV) and WHU-Hi-LongKou (LK). The results demonstrate that the proposed method outperforms several deep learning methods, achieving overall accuracies of 95.67%, 99.04%, 99.55% and 99.51%, respectively, even with fewer training samples.

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by 1.72%, 5.19%, and 1.94% in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods.

In the past, convolutional neural network (CNN) has become one of the most popular deep learning frameworks, and has been widely used in Hyperspectral image classification tasks. Convolution (Conv) in CNN uses filter weights to extract features in local receiving domain, and the weight parameters are shared globally, which more focus on the high‐frequency information of the image. Different from Conv, Transformer can obtain the long‐term dependence between long‐distance features through modelling, and adaptively focus on different regions. In addition, Transformer is considered as a low‐pass filter, which more focuses on the low‐frequency information of the image. Considering the complementary characteristics of Conv and Transformer, the two modes can be integrated for full feature extraction. In addition, the most important image features correspond to the discrimination region, while the secondary image features represent important but easily ignored regions, which are also conducive to the classification of HSIs. In this study, a complementary integrated Transformer network (CITNet) for hyperspectral image classification is proposed. Firstly, three‐dimensional convolution (Conv3D) and two‐dimensional convolution (Conv2D) are utilised to extract the shallow semantic information of the image. In order to enhance the secondary features, a channel Gaussian modulation attention module is proposed, which is embedded between Conv3D and Conv2D. This module can not only enhance secondary features, but suppress the most important and least important features. Then, considering the different and complementary characteristics of Conv and Transformer, a complementary integrated Transformer module is designed. Finally, through a large number of experiments, this study evaluates the classification performance of CITNet and several state‐of‐the‐art networks on five common datasets. The experimental results show that compared with these classification networks, CITNet can provide better classification performance.

Hyperspectral image (HSI) classification is a vital task in hyperspectral image processing and applications. Convolutional neural networks (CNN) are becoming an effective approach for categorizing hyperspectral remote sensing images as deep learning technology advances. However, traditional CNN usually uses a fixed kernel size, which limits the model’s capacity to acquire new features and affects the classification accuracy. Based on this, we developed a spectral segmentation-based multi-scale spatial feature extraction residual network (MFERN) for hyperspectral image classification. MFERN divides the input data into many non-overlapping sub-bands by spectral bands, extracts features in parallel using the multi-scale spatial feature extraction module MSFE, and adds global branches on top of this to obtain global information of the full spectral band of the image. Finally, the extracted features are fused and sent into the classifier. Our MSFE module has multiple branches with increasing ranges of the receptive field (RF), enabling multi-scale spatial information extraction at both fine- and coarse-grained levels. On the Indian Pines (IP), Salinas (SA), and Pavia University (PU) HSI datasets, we conducted extensive experiments. The experimental results show that our model has the best performance and robustness, and our proposed MFERN significantly outperforms other models in terms of classification accuracy, even with a small amount of training data.

Hyperspectral image (HSI) classification is the key technology of remote sensing image processing. In recent years, convolutional neural network (CNN), which is a powerful feature extractor, has been introduced into the field of HSI classification. Since the features of HSI are the basis of HSI classification, how to effectively extract the spectral-spatial features from HSI with CNN has become a research hotspot. The HSI feature extraction network, based on two-dimensional (2D) and three-dimensional (3D) CNN which can extract both spectral and spatial information, may lead to the increase of parameters and computational cost. Compared with 2D CNN and 3D CNN, the number of parameters and computational cost of one-dimensional (1D) CNN will be greatly reduced. However, 1D CNN based algorithms can only extract the spectral information without considering the spatial information. Therefore, in this paper, a lightweight multilevel feature fusion network (LMFFN) is proposed for HSI classification, which aims to achieve efficient extraction of spectral-spatial features and to minimize the number of parameters. The main contributions of this paper are divided into the following two points: First, we design a hybrid spectral-spatial feature extraction network (HSSFEN) to combine the advantages of 1D, 2D and 3D CNN. It introduces the idea of depthwise separable convolution method, which effectively reduces the complexity of the proposed HSSFEN. Then, a multilevel spectral-spatial feature fusion network (MSSFFN) is proposed to further obtain more effective spectral-spatial features, which effectively fuses the bottom spectral-spatial features and the top spectral-spatial features. To demonstrate the performance of our proposed method, a series of experiments are conducted on three HSI datasets, including Indian Pine, University of Pavia, and Salinas Scene datasets. The experimental results indicate that our proposed LMFFN is able to achieve better performance than the manual feature extraction methods and deep learning methods, which demonstrates the superiority of our proposed method.

Hyperspectral image (HSI) classification has been a hot topic for decides, as hyperspectral images have rich spatial and spectral information and provide strong basis for distinguishing different land-cover objects. Benefiting from the development of deep learning technologies, deep learning based HSI classification methods have achieved promising performance. Recently, several neural architecture search (NAS) algorithms have been proposed for HSI classification, which further improve the accuracy of HSI classification to a new level. In this paper, NAS and Transformer are combined for handling HSI classification task for the first time. Compared with previous work, the proposed method has two main differences. First, we revisit the search spaces designed in previous HSI classification NAS methods and propose a novel hybrid search space, consisting of the space dominated cell and the spectrum dominated cell. Compared with search spaces proposed in previous works, the proposed hybrid search space is more aligned with the characteristic of HSI data, that is, HSIs have a relatively low spatial resolution and an extremely high spectral resolution. Second, to further improve the classification accuracy, we attempt to graft the emerging transformer module on the automatically designed convolutional neural network (CNN) to add global information to local region focused features learned by CNN. Experimental results on three public HSI datasets show that the proposed method achieves much better performance than comparison approaches, including manually designed network and NAS based HSI classification methods. Especially on the most recently captured dataset Houston University, overall accuracy is improved by nearly 6 percentage points. Code is available at: https://github.com/Cecilia-xue/HyT-NAS.

In hyperspectral image (HSI) classification task, effectively deriving and incorporating spatial information into spectral features is one of a key focus as it can largely influence the performance. Markov random fields (MRFs) are generative and flexible image texture models, and capable of effectively extracting spatial neighbourhood information along multiple spectral wavebands in an unsupervised way. Its parameter estimation process also shares strong compatibility with deep architecture, especially the convolutional neural networks. In this work, we propose an MRF based spectral-spatial fusion network (SSFNet) for HSI classification. Spatial features are extracted using MRF models and further fused with spectral information. Then the proposed SSFNet takes the fused features as input and produces reliable classification results. Comprehensive experiments conducted on the Indian pines and the Pavia university datasets are reported to verify the proposed method.

Convolutional neural networks (CNNs) for hyperspectral image (HSI) classification have generated good progress. Meanwhile, graph convolutional networks (GCNs) have also attracted considerable attention by using unlabeled data, broadly and explicitly exploiting correlations between adjacent parcels. However, the CNN with a fixed square convolution kernel is not flexible enough to deal with irregular patterns, while the GCN using the superpixel to reduce the number of nodes will lose the pixel-level features, and the features from the two networks are always partial. In this paper, to make good use of the advantages of CNN and GCN, we propose a novel multiple feature fusion model termed attention multi-hop graph and multi-scale convolutional fusion network (AMGCFN), which includes two sub-networks of multi-scale fully CNN and multi-hop GCN to extract the multi-level information of HSI. Specifically, the multi-scale fully CNN aims to comprehensively capture pixel-level features with different kernel sizes, and a multi-head attention fusion module is used to fuse the multi-scale pixel-level features. The multi-hop GCN systematically aggregates the multi-hop contextual information by applying multi-hop graphs on different layers to transform the relationships between nodes, and a multi-head attention fusion module is adopted to combine the multi-hop features. Finally, we design a cross attention fusion module to adaptively fuse the features of two sub-networks. AMGCFN makes full use of multi-scale convolution and multi-hop graph features, which is conducive to the learning of multi-level contextual semantic features. Experimental results on three benchmark HSI datasets show that AMGCFN has better performance than a few state-of-the-art methods.