Hyperspectral Image Classification Research Articles

Advancing fraud detection in New Zealand Mānuka honey: Integrating hyperspectral imaging and GANomaly-based one-class classification

New Zealand Mānuka honey has become a prime target for adulteration due to its high commercial value. Given the diverse possibilities of fraudulent activities, training a supervised model that exhaustively covers all potential fraud scenarios is challenging. This study presents a new method for detecting fraudulent behavior in New Zealand Mānuka honey by combining hyperspectral imaging (HSI) with the GANomaly-based One-Class Classification method. We collected 18 different UMF-graded pure Mānuka honey samples from five New Zealand brands, which were used for training. The model was tested on fraudulent honey, including aged and syrup-adulterated honey, and compared with the traditional One-Class Classification methods. The results demonstrate that the HSI combined with the GANomaly method achieved 100% discrimination for all test samples, outperforming the standard rival techniques. In conclusion, this research developed a versatile model capable of detecting honey fraudulent behavior, showing significant practical implications for honey quality assessment.

Dual-stream spectral-spatial convolutional neural network for hyperspectral image classification and optimal band selection

Along with the high spectral rich information it provides, one of the difficulties in processing a hyperspectral image is the need for expert knowledge and high-spec hardware to process very high-dimensional data. 3D convolutional neural network(3D CNN), which uses spectral and spatial features together, enables a powerful solution for HSI classification. This study proposes an efficient dual-stream 3D CNN for accurate HSI classification.The proposed method offers effective classification using spectral-spatial features without relying on pre-processing or post-processing. A comparative study of how CNN classification performance is affected by hyperspectral band selection based on deep reinforcement learning (DRL) is presented. Using the most relevant bands in the hyperspectral image is decisive in deep CNN networks without losing information and accuracy. The proposed method was compared with 3D CNN, 3D + 1D CNN, Multiscale 3D deep convolutional neural network (M3D-DCNN), and InceptionV3 algorithms using Indian Pines (IP), Salinas, Pavia Center (PaviaC), Houston 2013 and QUH-Tangdaowan datasets. It achieved 92.43 % overall accuracy (OA) in IP, 95.06 % OA in Salinas dataset, 99.00 % OA in PaviaC dataset, 91.25 % OA in Houston 2013 and 94.87 % OA in QUH-Tangdaowan. Codes are released at: https://github.com/lapistlazuli/DS-3DCNN.

MSTSENet: Multiscale Spectral–Spatial Transformer with Squeeze and Excitation network for hyperspectral image classification

Hyperspectral image (HSI) classification pertains to the task of assigning a single label to each pixel by analyzing its spectral–spatial characteristics. Convolutional Neural Networks (CNNs) have garnered significant attention on account of their remarkable performance in feature representation. However, these approaches possess a restricted capacity to acquire highly similar spectral features. Recently, Attention-based approaches have been devised as a means to surmount the limitations inherent in CNNs. In this paper, we address a novel architecture for spectral–spatial feature extraction and HSI classification. The framework efficiently amalgamates the robustness and efficacy of three modern backbone networks, i.e., Multiscale CNN, Squeeze and Excitation (SE), and Transformer named MSTSENet. Initially, the multiscale convolution module integrates three branches of 3D convolution layers, each employing distinct kernel sizes, facilitating multiscale and multi-resolution feature extraction. Subsequently, the SE module enhances the inter-channel relationship by adaptively recalibrating the feature weight. The strategic fusion of the SE module with multiscale CNN strengthens deep feature extraction while introducing a minimal increment in overall parameters. Lastly, the Transformer module orchestrates information aggregation across different spectral–spatial regions, accurately modeling long-range contextual dependencies and capturing dominant feature representations. To evaluate the model performance, comprehensive experiments have been carried out on widely used benchmark HSI datasets. The overall accuracy of 98.74%, 99.83%, and 99.98% is achieved over the Indian Pines, Pavia University, and Salinas Valley datasets, respectively. The code of this work will be accessible at https://github.com/irfan01000/MSTSENet.

A multi-scale multi-channel CNN introducing a channel-spatial attention mechanism hyperspectral remote sensing image classification method

ABSTRACT Aiming the problems that the classification performance of hyperspectral images in existing classification algorithms is highly dependent on spatial-spectral information and that detailed features are ignored in single convolutional channel feature extraction, resulting in poor generalization performance of the feature extraction model, a multi-scale multi-channel convolutional neural network (MMC-CNN) model is proposed in this paper. First, the data set is divided into two kinds of pixel module, and then different channels are used for feature extraction. A channel-space attention mechanism module is also introduced, and a multi-scale multichannel convolutional neural network (CSAM-MMC) model with the introduction of channel-space attention mechanism is proposed for deeper spatial-spectral feature extraction of hyperspectral image elements while reducing the redundancy of convolutional pooling parameters to achieve better HSI classification. To evaluate the effectiveness of the proposed model, experiments were conducted on Indian Pines, Pavia Center and KSC datasets respectively, and the overall classification accuracies of this paper’s MMC-CNN model in the HSI dataset were 97.23%, 98.50%, and 97.85%, thus verifying the model’s high feature extraction accuracy. The CSAM-MMC model in this paper further improves 0.13%, 0.35%, and 0.71% relative to the MMC-CNN model, which provides higher overall accuracies relative to other state-of-the-art algorithms.

Deep Learning based Feed Forward Neural Network Models for Hyperspectral Image Classification

Introduction Traditional feed-forward neural networks (FFNN) have been widely used in image processing, but their effectiveness can be limited. To address this, we develop two deep learning models based on FFNN: the deep backpropagation neural network classifier (DBPNN) and the deep radial basis function neural network classifier (DRBFNN), integrating convolutional layers for feature extraction. Methods We apply a training algorithm to the deep, dense layers of both classifiers, optimizing their layer structures for improved classification accuracy across various hyperspectral datasets. Testing is conducted on datasets including Indian Pine, University of Pavia, Kennedy Space Centre, and Salinas, validating the effectiveness of our approach in feature extraction and noise reduction. Results Our experiments demonstrate the superior performance of the DBPNN and DRBFNN classifiers compared to previous methods. We report enhanced classification accuracy, reduced mean square error, shorter training times, and fewer epochs required for convergence across all tested hyperspectral datasets. Conclusion The results underscore the efficacy of deep learning feed-forward classifiers in hyperspectral image processing. By leveraging convolutional layers, the DBPNN and DRBFNN models exhibit promising capabilities in feature extraction and noise reduction, surpassing the performance of conventional classifiers. These findings highlight the potential of our approach to advance hyperspectral image classification tasks.

Hyperspectral Image Classification Based on Adaptive Global–Local Feature Fusion

Labeled hyperspectral image (HSI) information is commonly difficult to acquire, so the lack of valid labeled data becomes a major puzzle for HSI classification. Semi-supervised methods can efficiently exploit unlabeled and labeled data for classification, which is highly valuable. Graph-based semi-supervised methods only focus on HSI local or global data and cannot fully utilize spatial–spectral information; this significantly limits the performance of classification models. To solve this problem, we propose an adaptive global–local feature fusion (AGLFF) method. First, the global high-order and local graphs are adaptively fused, and their weight parameters are automatically learned in an adaptive manner to extract the consistency features. The class probability structure is then used to express the relationship between the fused feature and the categories and to calculate their corresponding pseudo-labels. Finally, the fused features are imported into the broad learning system as weights, and the broad expansion of the fused features is performed with the weighted broad network to calculate the model output weights. Experimental results from three datasets demonstrate that AGLFF outperforms other methods.

Adaptive Learnable Spectral–Spatial Fusion Transformer for Hyperspectral Image Classification

In hyperspectral image classification (HSIC), every pixel of the HSI is assigned to a land cover category. While convolutional neural network (CNN)-based methods for HSIC have significantly enhanced performance, they encounter challenges in learning the relevance of deep semantic features and grappling with escalating computational costs as network depth increases. In contrast, the transformer framework is adept at capturing the relevance of high-level semantic features, presenting an effective solution to address the limitations encountered by CNN-based approaches. This article introduces a novel adaptive learnable spectral–spatial fusion transformer (ALSST) to enhance HSI classification. The model incorporates a dual-branch adaptive spectral–spatial fusion gating mechanism (ASSF), which captures spectral–spatial fusion features effectively from images. The ASSF comprises two key components: the point depthwise attention module (PDWA) for spectral feature extraction and the asymmetric depthwise attention module (ADWA) for spatial feature extraction. The model efficiently obtains spectral–spatial fusion features by multiplying the outputs of these two branches. Furthermore, we integrate the layer scale and DropKey into the traditional transformer encoder and multi-head self-attention (MHSA) to form a new transformer with a layer scale and DropKey (LD-Former). This innovation enhances data dynamics and mitigates performance degradation in deeper encoder layers. The experiments detailed in this article are executed on four renowned datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and the University of Pavia (UP). The findings demonstrate that the ALSST model secures optimal performance, surpassing some existing models. The overall accuracy (OA) is 99.70%, 89.72%, 97.84%, and 99.78% on four famous datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and University of Pavia (UP), respectively.

Hyperspectral Image Classification Based on Two-Branch Multiscale Spatial Spectral Feature Fusion with Self-Attention Mechanisms

In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model’s ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions.

Retraction Note: Effect of Legendre–Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification

Retraction Note: Effect of Legendre–Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification

LCTCS: Low-Cost and Two-Channel Sparse Network for Hyperspectral Image Classification

Using convolutional neural networks (CNNs) in classifying hyperspectral images (HSIs) has achieved quite good results in recent years. It is widely used in agricultural remote sensing, geological exploration, environmental monitoring, and marine remote sensing. Unfortunately, the complexity of network structures used for hyperspectral image classification challenges the efficient delivery of HSI data extremely, and existing methods suffer from a large amount of redundancy in the network weight parameters during training, as they either require huge computational resources or make inefficient use of storage space when designing the network structure, and many of the parameters that waste computational resources contribute less to the rich spectral and spatial information transfer in HSI. So we introduce LCTCS, a better low-memory and less-parametric network approach. LCTCS aims to improve the efficiency of computational resource utilization with advanced classification performance and lower levels of computational resources. Unlike the conventional 2D and 3D convolution used previously, we use simple and efficient 3D grouped convolution as a vehicle to convey the semantic features of HSIs. More specifically, we design a novel two-channel sparse network to classify HSIs since grouped 3D convolution conveys the properties of hyperspectral data well in the time and space domains.We have compared LCTCS with eight widely used network methods on four publicly available hyperspectral datasets for learning HSI information. A series of experiments shows that the model architecture designed has 65.89%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$65.89 \\%$$\\end{document} less storage space than the DBDA method, consumes 67.36%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$67.36 \\%$$\\end{document} fewer computational resources than the SSRN method on the IP dataset, and accomplishes a highly accurate classification task with the number of parameters accounting for only 1.99%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.99 \\%$$\\end{document} that of the DBMA method.

Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields.

DMAF-NET: Deep Multi-Scale Attention Fusion Network for Hyperspectral Image Classification with Limited Samples

In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several critical issues that need to be addressed in the HSIC task, such as the lack of labeled training samples, which constrains the classification accuracy and generalization ability of CNNs. To address this problem, a deep multi-scale attention fusion network (DMAF-NET) is proposed in this paper. This network is based on multi-scale features and fully exploits the deep features of samples from multiple levels and different perspectives with an aim to enhance HSIC results using limited samples. The innovation of this article is mainly reflected in three aspects: Firstly, a novel baseline network for multi-scale feature extraction is designed with a pyramid structure and densely connected 3D octave convolutional network enabling the extraction of deep-level information from features at different granularities. Secondly, a multi-scale spatial–spectral attention module and a pyramidal multi-scale channel attention module are designed, respectively. This allows modeling of the comprehensive dependencies of coordinates and directions, local and global, in four dimensions. Finally, a multi-attention fusion module is designed to effectively combine feature mappings extracted from multiple branches. Extensive experiments on four popular datasets demonstrate that the proposed method can achieve high classification accuracy even with fewer labeled samples.

Deciphering Hyperspectral Image Classification: A Comprehensive Exploration

Deciphering Hyperspectral Image Classification: A Comprehensive Exploration

Enhancing Semi-Supervised Few-Shot Hyperspectral Image Classification via Progressive Sample Selection

Hyperspectral images (HSIs) provide valuable spatial–spectral information for ground analysis. However, in few-shot (FS) scenarios, the limited availability of training samples poses significant challenges in capturing the sample distribution under diverse environmental conditions. Semi-supervised learning has shown promise in exploring the distribution of unlabeled samples through pseudo-labels. Nonetheless, FS HSI classification encounters the issue of high intra-class spectral variability and inter-class spectral similarity, which often lead to the diffusion of unreliable pseudo-labels during the iterative process. In this paper, we propose a simple yet effective progressive pseudo-label selection strategy that leverages the spatial–spectral consistency of HSI pixel samples. By leveraging spatially aligned ground materials as connected regions with the same semantic and similar spectrum, pseudo-labeled samples were selected based on round-wise confidence scores. Samples within both spatially and semantically connected regions of FS samples were assigned pseudo-labels and joined subsequent training rounds. Moreover, considering the spatial positions of FS samples that may appear in diverse patterns, to fully utilize unlabeled samples that fall outside the neighborhood of FS samples but still belong to certain connected regions, we designed a matching active learning approach for expert annotation based on the temporal confidence difference. We identified samples with the highest training value in specific regions, utilizing the consistency between predictive labels and expert labels to decide whether to include the region or the sample itself in the subsequent semi-supervised iteration. Experiments on both classic and more recent HSI datasets demonstrated that the proposed base model achieved SOTA performance even with extremely rare labeled samples. Moreover, the extended version with active learning further enhances performance by involving limited additional annotation.

Enhanced Wild Horse Optimizer with Cauchy Mutation and Dynamic Random Search for Hyperspectral Image Band Selection

The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information content and low noise. The wild horse optimizer (WHO) is a novel metaheuristic algorithm widely used for its efficient search performance, yet it tends to become trapped in local optima during later iterations. To address these issues, an enhanced wild horse optimizer (IBSWHO) is proposed for HSI band selection in this paper. IBSWHO utilizes Sobol sequences to initialize the population, thereby increasing population diversity. It incorporates Cauchy mutation to perturb the population with a certain probability, enhancing the global search capability and avoiding local optima. Additionally, dynamic random search techniques are introduced to improve the algorithm search efficiency and expand the search space. The convergence of IBSWHO is verified on commonly used nonlinear test functions and compared with state-of-the-art optimization algorithms. Finally, experiments on three classic HSI datasets are conducted for HSI classification. The experimental results demonstrate that the band subset selected by IBSWHO achieves the best classification accuracy compared to conventional and state-of-the-art band selection methods, confirming the superiority of the proposed BS method.

Spectral Spatial Neighborhood Attention Transformer for Hyperspectral Image Classification

Hyperspectral image (HSI) can provide rich spectral information, which can be helpful for accurate classification in many applications. However, the hyperspectral image classification task has challenges, including limited labeled data, data redundancy, data sparsity, and imbalanced class samples. Over time, various methods have been proposed to solve the above-mentioned problems. To mitigate the issue mentioned above in this paper, we present the neighborhood attention transformer with a channel-wise shift technique for hyperspectral image classification. The neighborhood attention transformer leverages the power of the attention mechanism to capture the spatial relationship between neighboring pixels and extract discriminative features. The channel-wise shift techniques empower the model to adjust the spectral characteristics of each channel adaptively, enhancing its ability to handle the spectral variations present in hyperspectral data. To validate the effectiveness of the proposed model, we conduct comprehensive experiments on the publically available dataset. The results demonstrate that our model consistently outperforms other state-of-the-art methods. The overall accuracy of the proposed model reached 99.41 % , 99.93 % , 98.35 % 99.59 % four datasets.

Multi-hop double-branch network for small sample hyperspectral image classification

ABSTRACT Neural networks have been extensively utilized in the classification of hyperspectral images. The study introduces a novel dual-branch structured network named MGACN for hyperspectral image classification under the condition of small sample sizes. This model a multi-hop graph network (MHGCN) and a graph attention convolutional network (GACNN). Graph Convolutional Network (GCN) integrates multi-hop mechanism to obtain and aggregate information between long-distance non-neighbor nodes. The Graph Attention Network (GAT) combines 2D-CNN by incorporating a noise reduction mechanism, as well as position and channel attention mechanisms. The classification accuracy reaches 98.43%, 99.02%, and 98.55% on the Indian Pines, Salinas, and Pavia University datasets, which is significantly better than that of the comparison model.

Hyperspectral image classification based on superpixel merging and broad learning system

AbstractMost spectral–spatial classification methods for hyperspectral images (HSIs) can achieve satisfactory classification results. However, the common problem faced with these approaches is the need for a long training time and sufficient training samples. To address this issue, this study proposes an effective spectral–spatial HSI classification method based on superpixel merging, superpixel smoothing and broad learning system (SMS‐BLS). The newly introduced parameter‐free superpixel merging technique based on local modularity not only enhances the role of local spatial information in classification, but also maintains class boundary information as much as possible. In addition, the spectral and spatial information of HSIs is further fused during the superpixel smoothing process. As a result, with limited training samples, using merged and smoothed superpixels instead of pixels as input to the broad learning system significantly improves its classification performance. Moreover, the merged superpixels weaken the dependence of the classification results on the superpixel segmentation scale. The effectiveness of the proposed method was validated on three HSI benchmarks, namely Indian Pines, Pavia University and Salinas. Experimental and comparative results show the superiority of the method to other state‐of‐the‐art approaches in terms of overall accuracy and running time.

Full-scale semantic segmentation of hyperspectral imaging based on spatial spatial-spectral joint network

Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification. This article utilizes an encoding decoding structure to simultaneously extract deep and shallow features of images. A REGCS convolution module was constructed using the idea of group convolution to extract spectral and spatial features of images. We compared the Salinas Valley dataset and MUUFL dataset with various classification algorithms. The experimental results show that compared with other classification models, the RESSU model has achieved stable and excellent results in hyperspectral image classification experiments. Among them, in the classification experiment of the Salinas Valley dataset, the accuracy of single class classification reached over 92%. In the effectiveness analysis experiment, we calculated different model parameter quantities to verify the performance of our method, and ultimately achieved good results.

Discriminating Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Review.

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral-spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral-spatial representations for classification. In this article, the crucial factors to discriminate spectral-spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral-spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.