Hyperspectral image classification based on combining texture and multiscale guided filter feature of principal component

<strong class="journal-contentHeaderColor">Abstract.</strong> Hyperspectral images contain abundant spectral and spatial information about the earth's surface, labeling data processing and analysis more difficult, as well as the problem of sample labeling. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced, and a sample labeling method based on neighborhood information and priority classifier discrimination is presented. Then, a hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented. The LBP is employed to extract features of spatial texture information from remote sensing images and enrich the feature information of samples. Then the multivariate logistic regression model is used to select the unlabeled samples with the largest amount of information, and the unlabeled samples with neighborhood information and priority classifier tags are selected to obtain the pseudo-labeled samples after learning. By making full use of the advantages of sparse representation and mixed logistic regression model, a new hyperspectral remote sensing image classification model based on semi-supervised learning is constructed to effectively achieve accurate classification of hyperspectral images. The data of Indian Pines, Salinas scene and Pavia University are selected to verify the validity of the proposed method. The experiment results show that the proposed classification method can obtain higher classification accuracy and show stronger timeliness and generalization ability.

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.

Hyperspectral image classification using multi-feature fusion

In this research, a deep learning approach for hyperspectral image (HSI) classification was developed, incorporating attention mechanisms, multiscale feature learning, and utilization of unsampled pixels. The proposed model, multiscale attention-based hybrid spectral network and UNet (MSA-HybridSN-U), was evaluated on three benchmark datasets: Indian Pines, University of Pavia, and University of Houston using Overall Accuracy (OA), Average Accuracy (AA), and Kappa index (K). The proposed model MSA-HybridSN-U had high accuracy on Indian Pines (IP), Pavia University (PU), and Houston University (HU) datasets. It achieved the highest average accuracy compared with other models like 3D CNN, HybridSN, M3D-DCNN, DBDA, and ACA-HybridSN and higher than 2D CNN and SVM. For the IP dataset, the OA and AA scores are both 99.71% and 99.65% respectively, indicating a high level of accuracy in classifying the samples. The Kappa statistic, which measures inter-annotator agreement, is also high at 0.997, suggesting that the method is consistent in its predictions. For the PU dataset, the results are even higher with OA and AA scores of 99.97% and 99.89% respectively, and a Kappa statistic of 0.999, indicating even higher accuracy and consistency. Finally, for the HU dataset, the results are similarly high with OA and AA scores of 99.47% and 99.92% respectively, and a Kappa statistic of 0.994. The use of attention mechanisms, multiscale features, and unsampled pixels has improved the classification performance of the model. The combination of spectral and spatial attention modules improved the accuracy the most, with the highest accuracy of 99.97% on the PU dataset. Utilizing unsampled pixels in the classification process resulted in a noticeable improvement in accuracy, particularly on the HU dataset with an improvement of 0.29%. The results show the effectiveness of combining multiscale features and attention modules in improving the accuracy of HSI classification.

Due to the high computational complexity of traditional convolutional neural networks, the execution time is long and the computational cost is too high. In this paper, we propose a deep separable convolutional neural network with attention mechanism added to improve the classification accuracy and generalization ability of hyperspectral images. The network uses separable convolution combined with residual connections to construct residual units with fewer parameters and adds an attention mechanism layer at the end of the network, which helps to improve the overall performance of the model. So this model has stronger generalization ability now, shorter computation time, and stronger network performance. Finally, the overall accuracy of the model in this paper is 98.48%, 99.1% and 97.40% on the Salinas dataset and the more newly proposed Wuhan Longkou and Wuhan Hanchuan datasets, respectively. It proves that the model has better generalization ability and can complete the calculation in a shorter time. Improving the classification accuracy of hyperspectral images like the Wuhan Longkou dataset is important for agricultural development.KeywordsResidual NetworkSeparable convolutionConvolutional neural networkAttention mechanismAgricultural hyperspectrum

Due to the 3-D nature of hyperspectral images, as well as the spatial properties (such as regularity and continuity) of land covers, many 3-D feature extraction operators have been designed to fully exploit the joint spatial–spectral information. However, the large amount of obtained features can suffer from the “curse of dimensionality” problem, especially for the small training sample set. Moreover, various spatial–spectral features can represent the characteristics of the hyperspectral image from different aspects. In this paper, a multiple 3-D feature fusion framework (M3DF3) has been proposed for hyperspectral image classification. First, we extend the 2-D Gabor surface feature into 3-D (3DSF) domains to comply with the spatial–spectral structure of the hyperspectral image, which is directly applied on the original hyperspectral image instead of the Gabor features. Second, three 3-D feature extraction methods, including the 3-D morphological profile, the 3-D local binary pattern, and the proposed 3DSF, that, respectively, characterize the hyperspectral image from three different angles, i.e., morphology, local dependence, and shape smoothness, are fused under a multitask sparse representation framework to take full advantage of the multiple 3-D features together. The proposed M3DF3 approach was fully tested on three real-world hyperspectral image data, i.e., the widely used Indian Pines, Pavia University, and Houston University. The results show that our method can achieve as high as 68.22%, 79.44%, and 72.84% accuracies, respectively, even when only few samples, i.e., three samples per class, are used for training.

Attention mechanism-based Hyperspectral Image (HSI) classification algorithms typically extract spectral and spatial features by spectral attention and spatial attention network respectively. However, these algorithms lack joint attention and ignore imbalanced samples, leading to insufficient information extraction. To address this problem, this paper proposes a novel HSI classification algorithm based on the pyramidal coordinate attention and weighted self-distillation (PCA-WSD). To perform the joint attention of spectral and spatial features, the proposed PCA mechanism uses spectral attention to cope with the diverse spatial features. The PCA mechanism consists of two components. First, the spatial pyramid coordinate squeeze (SPCS) is designed to aggregate spatial features with local and global information. Then, the tailored spatial pyramid coordinate excitation (SPCE) adaptively enhances their informative spectral features for the obtained spatial features, realizing the joint attention to spectral-spatial features. Further, considering the imbalance of samples, WSD is proposed. Specifically, weighted cross-entropy is integrated into WSD. Extensive experiments are evaluated on the four HSI benchmark datasets: Indian Pine (IP), Pavia University (UP), Kennedy Space Center (KSC), and Pavia Center (PC). Compared with the seven advanced algorithms, experimental results of the proposed algorithm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . reveal superior classification performance, especially for the imbalanced samples.

Local binary pattern (LBP) is a simple yet efficient image feature description algorithm that has been widely used in applications related to image classification. To more effectively make use of the local features of a hyperspectral image (HSI), a tensor-based spatial–spectral local binary pattern (TSSLBP) that can fully utilize the local spatial-spectral feature of an HSI is proposed. We specifically make efforts in three aspects. First, we construct a third-order tensor model for HSI to combine the local spatial and spectral features. Second, a dimension reduction method based on tensor decomposition is proposed to reduce redundant information of the HSI tensor model. Third, a joint spatial-spectral LBP coding algorithm is implemented based on the HSI tensor model to calculate the local spatial–spectral feature vector of HSI. To verify the effectiveness of the proposed TSSLBP, we apply it to the task of HSI classification and compare it with six existing algorithms. Experimental results on the three popular HSI datasets show that TSSLBP can fully utilize rich information in the local spatial–spectral domain and can significantly improve the classification accuracy of HSI, especially in the case of scarce training samples.

In this paper, a novel method based on wavelet transformation of functional data for accurate classification of hyperspectral images is proposed. The motivation of the proposed method is that in the hyperspectral images, the spectral curve of each pixel can be viewed as a function, mathematically. And it can make the best of the abundant spectral information of the original hyperspectral images. Based on this, we have proposed an effective hyperspectral image classification method which mainly used functional principal component analysis (FPCA) before. However, FPCA method focuses on the global features analysis of hyperspectral image data and may not well extract local structural features. In this paper, in order to better reflect the local structural features of hyperspectral images, we make use of the time-frequency localization characteristics of wavelet transformation to decompose hyperspectral images. Then, the sparse wavelet decomposition coefficients are classified by using extreme learning machine (ELM). Experimental results on two popular hyperspectral images show that our method outperforms some state-of-the-art hyperspectral image classification methods.

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.

A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification

Deep learning has achieved remarkable success in hyperspectral image (HSI) classification, attributed to its powerful feature extraction capabilities. However, existing methods face several challenges: Convolutional Neural Networks (CNNs) are limited in modeling long-range spectral dependencies because of their limited receptive fields; Transformers are constrained by their quadratic computational complexity; and Mamba-based methods fail to fully exploit spatial–spectral interactions when handling high-dimensional HSI data. To address these limitations, we propose MRFP-Mamba, a novel Multi-Receptive-Field Parallel Mamba architecture that integrates hierarchical spatial feature extraction with efficient modeling of spatial–spectral dependencies. The proposed MRFP-Mamba introduces two key innovation modules: (1) A multi-receptive-field convolutional module employing parallel 1×1, 3×3, 5×5, and 7×7 kernels to capture fine-to-coarse spatial features, thereby improving discriminability for multi-scale objects; and (2) a parameter-optimized Vision Mamba branch that models global spatial–spectral relationships through structured state space mechanisms. Experimental results demonstrate that the proposed MRFP-Mamba consistently surpasses existing CNN-, Transformer-, and state space model (SSM)-based approaches across four widely used hyperspectral image (HSI) benchmark datasets: PaviaU, Indian Pines, Houston 2013, and WHU-Hi-LongKou. Compared with MambaHSI, our MRFP-Mamba achieves improvements in Overall Accuracy (OA) by 0.69%, 0.30%, 0.40%, and 0.97%, respectively, thereby validating its superior classification capability and robustness.

Hyperspectral remote sensing image classification based on residual generative Adversarial Neural Networks

Dual selective fusion transformer network for hyperspectral image classification.

Object-based classification methods can improve the accuracy of hyperspectral image classification due to the fact that they incorporate spatial information into the classification procedure by assigning neighboring pixels into the same class. In this paper, a new object-based feature extraction method is proposed which makes use of information theory to reduce the Bayes error. In this way, the proposed method exploits higher order statistics for feature extraction which are very effective for non Gaussian data such as hyperspectral images. The criterion to be minimized is composed of three mutual information terms. The first and second terms, consider the maximal relevance and minimal redundancy, respectively, while the third term takes into account the segmentation map containing disjoint spatial regions. To obtain the segmentation map, we apply the firefly clustering algorithm whose fitness function simultaneously considers the intra-distance between samples and their cluster centroids, and inter-distance between centroids of any two clusters. Our experimental results, performed using a variety of hyperspectral scenes, indicate that the proposed framework gives better classification results than some state-of the-art spectral–spatial feature extraction methods.