SFCFNet: A Spatial–Frequency Cross-Attention Fusion Network for Hyperspectral Image Classification

Hyperspectral image classification faces multiple challenges in handling complex spectral and spatial features. Existing methods typically focus on either local or global feature extraction, often neglecting the synergy and the underutilisation of frequency domain features. Additionally, these methods show reduced accuracy and robustness with limited training samples. To address these issues, this paper proposes the graph attention and frequency aware siamese network, GATSiamNet, a dual branch network for few-shot hyperspectral image classification. The method extracts global features from a superpixel-based graph structure, while the other branch, based on a convolutional neural network, captures local details by integrating spectral-spatial and frequency domain features. This architecture not only captures multi-dimensional spectral-spatial and frequency domain but also enhances classification performance through an effective feature fusion strategy. Experiments on four benchmark hyperspectral datasets show that the proposed method outperforms state-of-the-art approaches, particularly under limited training samples, demonstrating strong generalisation. With five labelled samples per class, the overall accuracy on the Pavia University dataset is 91.04%, and 82.67% on the University of Houston dataset. Furthermore, ablation studies validate the critical role of frequency domain information, along with spectral and spatial feature fusion, in improving classification performance, proving the effectiveness of GATSiamNet in capturing multi-dimensional features.

Adaptive spectral-spatial feature fusion network for hyperspectral image classification using limited training samples

Hyperspectral image (HSI) classification is one of the most crucial tasks in remote sensing processing. The attention mechanism is preferable to a convolutional neural network (CNN), due to its superior ability to express information during HSI processing. Recently, numerous methods combining CNNs and attention mechanisms have been applied in HSI classification. However, it remains a challenge to achieve high-accuracy classification by fully extracting effective features from HSIs under the conditions of limited labeled samples. In this paper, we design a novel HSI classification network based on multiscale hybrid networks and attention mechanisms. The network consists of three subnetworks: a spectral-spatial feature extraction network, a spatial inverted pyramid network, and a classification network, which are employed to extract spectral-spatial features, to extract spatial features, and to obtain classification results, respectively. The multiscale fusion network and attention mechanisms complement each other by capturing local and global features separately. In the spatial pyramid network, multiscale spaces are formed through down-sampling, which can reduce redundant information while retaining important information. The structure helps the network better capture spatial features at different scales, and to improve classification accuracy. Experimental results on various public HSI datasets demonstrate that the designed network is extremely competitive compared to current advanced approaches, under the condition of insufficient samples.

For hyperspectral image (HSI) classification, two branch networks generally use the convolution neural networks (CNNs) to extract the spatial features and the long short-term memory (LSTM) to learn the spectral features. However, CNN with a local kernel neglects the global properties of the whole HSI. LSTM doesn't consider the macroscopic and detailed information of spectra. In this paper, we propose a dual-view spectral and global spatial feature fusion network (DSGSF) to extract the spatial-spectral features for HSI classification, including a spatial subnetwork and a spectral subnetwork. In the spatial subnetwork, we propose a global spatial feature representation model based on the encoder-decoder structure with channel attention and spatial attention to learn the global spatial features. In the spectral subnetwork, we design a dual-view spectral feature aggregation model with view attention to learn the diversity of spectral features. By fusing the two subnetworks, we construct DSGSF to extract the spatial-spectral features of HSI with strong discriminating performance. Experimental results on three public datasets illustrate that the proposed method can achieve competitive results compared with the state-of-the-art methods. Code: https://github.com/RZWang-WH/DSGSF.

Hyperspectral sensors provide an opportunity to capture the intensity of high spatial/spectral information and enable applications for high-level earth observation missions, such as accurate land cover mapping and target/object detection. Currently, convolutional neural networks (CNNs) are good at coping with hyperspectral image processing tasks because of the strong spatial and spectral feature extraction ability brought by hierarchical structures, but the convolution operation in CNNs is limited to local feature extraction in both dimensions. In the meanwhile, the introduction of the Transformer structure has provided an opportunity to capture long-distance dependencies between tokens from a global perspective; however, Transformer-based methods have a restricted ability to extract local information because they have no inductive bias, as CNNs do. To make full use of these two methods’ advantages in hyperspectral image processing, a dual-flow architecture named Hyper-LGNet to couple local and global features is firstly proposed by integrating CNN and Transformer branches to deal with HSI spatial-spectral information. In particular, a spatial-spectral feature fusion module (SSFFM) is designed to maximally integrate spectral and spatial information. Three mainstream hyperspectral datasets (Indian Pines, Pavia University and Houston 2013) are utilized to evaluate the proposed method’s performance. Comparative results show that the proposed Hyper-LGNet achieves state-of-the-art performance in comparison with the other nine approaches concerning overall accuracy (OA), average accuracy (AA) and kappa index. Consequently, it is anticipated that, by coupling CNN and Transformer structures, this study can provide novel insights into hyperspectral image analysis.

The hyperspectral image (HSI) classification applications become widespread in multiple applications. Therefore, the accurate classification of ground features using HSIs is an important research topic that has received a lot of interest. However, the conventional approaches failed to classify the pixels perfectly, which resulted in reduced accuracy. Thus, this work is focused on implementation of HSI classification using generative adversarial networks (HCGAN-Net). Initially, Gabor filtering is applied on HSIs for elimination of different types of noises, which also extracts the spatial features. Then, probabilistic principal component analysis (PCA) is applied to perform the dimensionality reduction operation, which also selects the best features using probability dependencies between spatial features. Then, spatial features are extracted for each spectral band and forms the hybrid spatial-spectral features. Finally, HCGAN-Net is used to performs the HSI classification operation using trained spatial-spectral features. The simulation results show that the proposed HCGAN-Net resulted in superior subjective and objective performance as compared to state of art HSI classifiers on various datasets.

Hyperspectral image (HSI) classification accuracy has been greatly improved by employing deep learning. The current research mainly focuses on how to build a deep network to improve the accuracy. However, these networks tend to be more complex and have more parameters, which makes the model difficult to train and easy to overfit. Therefore, we present a lightweight deep convolutional neural network (CNN) model called S2FEF-CNN. In this model, three S2FEF blocks are used for the joint spectral–spatial features extraction. Each S2FEF block uses 1D spectral convolution to extract spectral features and 2D spatial convolution to extract spatial features, respectively, and then fuses spectral and spatial features by multiplication. Instead of using the full connected layer, two pooling layers follow three blocks for dimension reduction, which further reduces the training parameters. We compared our method with some state-of-the-art HSI classification methods based on deep network on three commonly used hyperspectral datasets. The results show that our network can achieve a comparable classification accuracy with significantly reduced parameters compared to the above deep networks, which reflects its potential advantages in HSI classification.

Hyperspectral image (HSI) classification remains a challenging problem due to unique characteristics of HSI data (such as numerous bands and strong correlations in the spectral and spatial domains) and small sample size. To address such concerns, we propose a novel spectral–spatial feature extraction method for HSI classification by employing graph embedding and deep learning (DL) models. Since the conventional graph cannot capture the complex manifold relationship of HSI data, and there exist the observations of within-class variation as well as the similarity between different classes in the spectral domain, we construct the supervised within-class/between-class hypergraph (SWBH) to extract the spectral features of HSI. Since it is difficult for DL models to learn representative features for HSI data when the labeled training samples are limited, we propose the random zero settings to newly generate a large amount of labeled HSI samples for the training of convolutional neural network (CNN). The designed sample expanded CNN (SECNN) is used to extract the HSI spatial features. Thus, the spectral–spatial features of HSI can be learned by integrating the features extracted from SWBH and SECNN, respectively. Experiments on three real HSI datasets demonstrate higher classification accuracy of the proposed SWBH–SECNN method.

Hyperspectral data consists of continuous narrow spectral bands. Due to this, it has less spatial and high spectral information. Convolutional neural networks (CNNs) emerge as a highly contextual information model for remote sensing applications. Unfortunately, CNNs have constraints in their underlying network architecture in regards to the global correlation of spatial and spectral features, making them less reliable for mining and representing the sequential properties of spectral signatures. In this article, limpid size attention network (LSANet) is proposed, which contains 3D and 2D convolution blocks for enhancement of spatial-spectral features of the hyperspectral image (HSI). In addition, limpid attention block (LAB) is designed to provide a global correlation of the spectral and spatial features through LS attention. Furthermore, the computational costs of LS-attention are less compared to the multi-head self-attention (MHSA) of the classical vision transformer (ViT). In the ViT encoder a conditional position encoding (CPE) module is utilized that dynamically generates tokens from the feature maps to capture a richer contextual representation. The LSANet obtained overall accuracy (OA) of 98.78%, 98.67%, 97.52% and 89.45%, respectively, on the Indian Pines (IP), Pavia University (PU), Salina Valley (SV) and Botswana datasets. Our model’s quantitative and qualitative results are considerably better than the classical CNN and transformer-based methods.

Convolutional neural networks (CNNs) have been applied to learn spatial features for high-resolution (HR) synthetic aperture radar (SAR) image classification. However, there has been little work on integrating the unique statistical distributions of SAR images which can reveal physical properties of terrain objects, into CNNs in a supervised feature learning framework. To address this problem, a novel end-to-end supervised classification method is proposed for HR SAR images by considering both spatial context and statistical features. First, to extract more effective spatial features from SAR images, a new deep spatial context encoder network (DSCEN) is proposed, which is a lightweight structure and can be effectively trained with a small number of samples. Meanwhile, to enhance the diversity of statistics, the nonstationary joint statistical model (NS-JSM) is adopted to form the global statistical features. Specifically, SAR images are transformed into the Gabor wavelet domain and the produced multi-subbands magnitudes and phases are modeled by the log-normal and uniform distribution. The covariance matrix is further utilized to capture the inter-scale and intra-scale nonstationary correlation between the statistical subbands and make the joint statistical features more compact and distinguishable. Considering complementary advantages, a feature fusion network (Fusion-Net) base on group compression and smooth normalization is constructed to embed the statistical features into the spatial features and optimize the fusion feature representation. As a result, our model can learn the discriminative features and improve the final classification performance. Experiments on four HR SAR images validate the superiority of the proposed method over other related algorithms.

Hyperspectral Image (HSI) classification plays a key role remote sensing field. Recently, deep learning has demonstrated its effectiveness in HSI Classification field. This paper presents a spectral-spatial HSI classification technique established on the deep learning based deep belief network (DBN) for deep and abstract feature extraction and adaptive boundary adjustment based segmentation. Proposed approach focuses on integrating the deep learning based spectral features and segmentation based spatial features into a framework for improved performance. Specifically, first the deep DBN model is exploited as a spectral feature extraction based classifier to extract the deep spectral features. Second, spatial contextual features are obtained by utilizing effective adaptive boundary adjustment based segmentation technique. Finally, maximum voting based criteria is operated to integrate the results of extracted spectral and spatial information for improved HSI classification. In general, exploiting spectral features from DBN process and spatial features from segmentation and integration of spectral and spatial information by maximum voting based criteria, has a substantial effect on the performance of HSI classification. Experimental performance on real and widely used hyperspectral data sets with different contexts and resolutions demonstrates the accuracy of the proposed technique and performance is comparable to several recently proposed HSI classification techniques.

Hyperspectral image classification has been widely applied in many fields, but it also faces challenges because of small number of labeled samples. In this paper, we propose the Multiscale Multistage Spectral-Spatial Feature Fusion Framework (M^2 S^2 F^2 ) for hyperspectral image classification using small training samples. The Framework is the combination of two deep convolutional neural networks, which can extract more representative and discriminative features by combining the following operations. Firstly, two different scale 3-D cubes are the inputs for the spectral and spatial feature extraction respectively. Secondly, by fusing strong complementary information between different layers, we form multistage spectral and spatial features by fusion primary, intermediate and advanced features of the spectral and spatial features respectively. Spectral and spatial features are extracted by spectral and spatial skipped residual blocks, which can effectively alleviate the problems of gradient degradation. Thirdly, the fusion of complementary multistage spectral and spatial features can improve the classification accuracy. Experimental results on the IN, UP and KSC datasets show the effectiveness of the proposed method using small training samples.

Hyperspectral images (HSIs) classification methods based on convolutional neural networks are becoming increasingly popular. Many proposed methods extract spatial and spectral features simultaneously, and the interaction between the two types of features leads to unsatisfied classification results. Moreover, most of the existing CNN-based methods mainly consider single-scale, which can cause some important information to be neglected. To address the aforementioned two issues, we propose a Attention-Based Multi-Scale Network (AMSN) for HSIs classification. First, the proposed network is based on 3D-CNN, through channel branch and spatial branch, the AMSN can capture more distinctive spatial and spectral features using different convolution kernels, respectively. Second, Local and global features are extracted by dense network. Then, two features are concatenated to make full use of multi-scale features. Third, attention blocks are used after each branch to achieve the most distinctive features. Experimental results on three HSIs datasets demonstrate that the proposed framework can achieve better classification performance than the several state-of-the-art methods.

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.

In this study, we investigated how shapes are classified based on local and global features by four representative convolutional neural networks (CNNs), i.e., AlexNet, VGG, ResNet and Inception. While the local features are based on simple components, such as orientation of line segment, the global features are based on the whole object, such as whether an object has a hole. For example, solid triangles and solid squares are differentiated by local features, solid circles and rings are differentiated by a global feature. Two sets of experiments were performed in this research. In the first experiment, we examined how the four CNNs pre-trained on ImageNet (with transfer learning) learned to differentiate the regular shapes (equilateral-triangles, squares, circles and rings). Our results showed that the pre-trained CNNs exhibited faster learning rates in the tasks discriminating the local features than in the tasks discriminating the global feature. However, the transfer learning of discriminating the global feature in regular shapes were better generalized to irregular shapes than the transfer learning of discriminating local features. In the second experiment, the CNNs were trained from scratch (with random weights initialization) to discriminate local and global features in regular and irregular shapes. Different from the transfer learning, the CNNs exhibited faster learning rates in discriminating the global feature than the local features. Similar to transfer learning, the CNNs exhibited excellent learning generalization to discriminating the global feature of irregular shapes, but poor learning generalization to discriminating the local features in the irregular shapes. The overarching goal of this research is to create a paradigm and benchmark to directly compare how the CNNs and primate visual systems process geometrical invariants. In contrast to the ImageNet approach which employs natural images to train CNNs, we employed the “ShapeNet” approach which features geometrical shapes with well-defined properties. The ShapeNet approach will not only help elucidate the strengths and limitations of CNN computation, but also provide insights into visual information processing in the primates.