Accuracy Of Hyperspectral Image Classification Research Articles

Semantic Guided prototype learning for Cross-Domain Few-Shot hyperspectral image classification

Hyperspectral image(HSI) classification has found extensive application in the field of remote sensing. However, traditional HSI classification methods typically rely on a large number of labeled samples, incurring significant costs and requiring considerable time and effort for acquisition. To address this issue, this paper proposes a novel cross-domain few-shot learning method termed Semantic Guided Prototype Learning (SGPL), which aims to optimize prototype learning through the incorporation semantic information, thereby enhancing the classification accuracy of hyperspectral images in few-shot scenarios. The core idea of the SGPL method is to integrate semantic information into the prototype learning framework, thereby enhancing the expressive capacity of each category prototype. Specifically, HSI category labels are encoded via one-hot encoding, and the corresponding HSI features are concatenated with these encoded vectors to construct an augmented feature representation. These augmented feature vectors are subsequently fed into a prototype generator, which is designed to learn the generation of category prototypes from the concatenated features. Through the application of one-hot encoding and feature concatenation, SGPL effectively captures and leverages discriminative information between classes. Moreover, the proposed model exhibits superior performance on three different HSI datasets: Salinas, the University of Pavia(UP), and Indian Pines(IP) with overall classification accuracies of 91.27%, 85.84%, and 69.33%, respectively, outperforming other methods. The source code is available at https://github.com/AIYAU/SGPL.

Hyperspectral Image Classification Using Multi-Scale Lightweight Transformer

The distinctive feature of hyperspectral images (HSIs) is their large number of spectral bands, which allows us to identify categories of ground objects by capturing discrepancies in spectral information. Convolutional neural networks (CNN) with attention modules effectively improve the classification accuracy of HSI. However, CNNs are not successful in capturing long-range spectral–spatial dependence. In recent years, Vision Transformer (VIT) has received widespread attention due to its excellent performance in acquiring long-range features. However, it requires calculating the pairwise correlation between token embeddings and has the complexity of the square of the number of tokens, which leads to an increase in the computational complexity of the network. In order to cope with this issue, this paper proposes a multi-scale spectral–spatial attention network with frequency-domain lightweight Transformer (MSA-LWFormer) for HSI classification. This method synergistically integrates CNN, attention mechanisms, and Transformer into the spectral–spatial feature extraction module and frequency-domain fused classification module. Specifically, the spectral–spatial feature extraction module employs a multi-scale 2D-CNN with multi-scale spectral attention (MS-SA) to extract the shallow spectral–spatial features and capture the long-range spectral dependence. In addition, The frequency-domain fused classification module designs a frequency-domain lightweight Transformer that employs the Fast Fourier Transform (FFT) to convert features from the spatial domain to the frequency domain, effectively extracting global information and significantly reducing the time complexity of the network. Experiments on three classic hyperspectral datasets show that MSA-LWFormer has excellent performance.

Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging

In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets.

Spectral–Spatial Classification With Naive Bayes and Adaptive FFT for Improved Classification Accuracy of Hyperspectral Images

Spectral–Spatial Classification With Naive Bayes and Adaptive FFT for Improved Classification Accuracy of Hyperspectral Images

Hyperspectral image classification using a new deep learning model based on pseudo-3D block and depth separable 2D–3D convolution

In the task of classification of hyperspectral images (HSI), standard 2D and 3D convolutions increase the number of trainable parameters, making the model computationally complex, prone to overfitting when trained on smaller datasets, and demanding a large amount of memory. Thus, the cost of computation and memory consumption are bottlenecks of deep hybrid 2D–3D CNN models. This inspired us to propose a light weighted hybrid CNN model with fewer trainable parameters for efficient and accurate classification of HSI by introducing the concept of pseudo-3D block and depth separable 2D–3D convolution. The major components of the proposed framework are standard 3D-CNN, pseudo-3D block, depthwise separable 3D CNN, depth separable 2D CNN, and spatial pyramid pooling (SPP). Pseudo-3D block and depth separable 2D–3D convolution make the model computational and memory efficient by drastically reducing the number of trainable parameters. Further, the SPP layer is placed between the convolution layers and fully connected layers to generate a feature vector of fixed length at different scales. The proposed model is applied to three publicly available datasets. Experimental analysis proves the efficacy of the proposed approach over the state-of-the-art methods.

Fast and Accurate Hyperspectral Image Classification with Window Shape Adaptive Singular Spectrum Analysis

Hyperspectral classification is a task of significant importance in the field of remote sensing image processing, with attaining high precision and rapid classification increasingly becoming a research focus. The classification accuracy depends on the degree of raw HSI feature extraction, and the use of endless classification methods has led to an increase in computational complexity. To achieve high accuracy and fast classification, this study analyzes the inherent features of HSI and proposes a novel spectral–spatial feature extraction method called window shape adaptive singular spectrum analysis (WSA-SSA) to reduce the computational complexity of feature extraction. This method combines similar pixels in the neighborhood to reconstruct every pixel in the window, and the main steps are as follows: rearranging the spectral vectors in the irregularly shaped region, constructing an extended trajectory matrix, and extracting the local spatial and spectral information while removing the noise. The results indicate that, given the small sample sizes in the Indian Pines dataset, the Pavia University dataset, and the Salinas dataset, the proposed algorithm achieves classification accuracies of 97.56%, 98.34%, and 99.77%, respectively. The classification speed is more than ten times better than that of other methods, and a classification time of only about 1–2 s is needed.

Hyperspectral Image Classification Using Geodesic Spatial–Spectral Collaborative Representation

With the continuous advancement of remote sensing technology, the information encapsulated within hyperspectral images has become increasingly enriched. The effective and comprehensive utilization of spatial and spectral information to achieve the accurate classification of hyperspectral images presents a significant challenge in the domain of hyperspectral image processing. To address this, this paper introduces a novel approach to hyperspectral image classification based on geodesic spatial–spectral collaborative representation. It introduces geodesic distance to extract spectral neighboring information from hyperspectral images and concurrently employs Euclidean distance to extract spatial neighboring information. By integrating collaborative representation with spatial–spectral information, the model is constructed. The collaborative representation coefficients are obtained by solving the model to reconstruct the testing samples, leading to the classification results derived from the minimum reconstruction residuals. Finally, with comparative experiments conducted on three classical hyperspectral image datasets, the effectiveness of the proposed method is substantiated. On the Indian Pines dataset, the proposed algorithm achieved overall accuracy (OA) of 91.33%, average accuracy (AA) of 93.81%, and kappa coefficient (Kappa) of 90.13%. In the case of the Salinas dataset, OA was 95.62%; AA was 97.30%; and Kappa was 93.84%. Lastly, on the PaviaU dataset, OA stood at 95.77%; AA was 94.13%; and Kappa was 94.38%.

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

Classification of hyperspectral images is an essential application of deep learning techniques. However, standard deep learning approaches require a large number of labelled samples for model training, and classification performance can be enhanced. In this study, we propose the use of residual generative adversarial networks for the classification of hyperspectral images based on deep learning Convolutional Neural Networks, a large number of labelled samples, and the high classification accuracy required for the classification task. The method is based on generative adversarial networks, with a 6-layer residual network containing up-sampling and convolutional layers replacing the inverse convolutional layer network structure of the generator to enhance the data generation capability, and an 18-layer residual convolutional network replacing the convolutional layer network structure of the discriminator to enhance the feature extraction capability. The hyperspectral image classification method with residual generation adversarial network enhances the information exchange between the shallow network and the deep network by adding a residual structure to the network, extracts the deep features of hyperspectral images and improves the accuracy of hyperspectral image classification. Extensive experiments on several benchmark hyperspectral datasets have shown that the method outperforms comparative methods by 0.7% to 22.3% on OA.

Hyperspectral image classification using improved multi-scale block local binary pattern and bi-exponential edge-preserving smoother

ABSTRACT In this paper, a multi-strategy fusion (MSF) framework, based on improved MBLBP and bi-exponential edge-preserving smoother (BEEPS), is proposed for hyperspectral image (HSI) classification. First, MBLBP operator is adopted to characterize the overall structural information of HSI, where the averaging strategy allocates same weights for the pixels in a local sub-region, so that the edges tend to be blurred due to being isotropic. To solve this question, the steering kernel is first introduced into MBLBP for learning the local structure prior of HSI. Then, a support vector machine classifier is used to calculate the soft classified probabilities of pixels. Furthermore, BEEPS is adopted to smooth the soft classified probabilities maps in the post-processing stage, and the purpose is to further improve classification accuracy of HSI by considering context-aware information for each class label. Experiments are performed on three real hyperspectral datasets, namely, Indian Pines, KSC, and Houston 2013, only 1%, 6, and 5 labeled samples are randomly selected for training, the overall accuracy(kappa) obtained by MSF is 99.47%(99.40), 99.52%(99.47), and 94.25%(93.78), respectively, which is far better than the contrast methods.

Attention-based multiscale deep learning with unsampled pixel utilization for hyperspectral image classification

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.

Improving Classification Accuracy of Hyperspectral Image Using Convolutional Neural Networks

Improving Classification Accuracy of Hyperspectral Image Using Convolutional Neural Networks

Multi-scale receptive fields: Graph attention neural network for hyperspectral image classification

Hyperspectral image (HSI) classification has attracted wide attention in many fields. Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers, which has improved the HSI classification accuracy greatly. However, GNN-based methods have not been widely applied due to their time-consuming, inefficient information description as well as poor anti-noise robustness. To overcome the deficiencies, a novel multi-scale receptive fields graph attention neural network (MRGAT) is proposed for HSI classification in this paper. In this network, a superpixel segment method is adopted to abstract the original HSI local spatial features. A two-layer one-dimensional convolution neural network (1D CNN) spectral transformer mechanism, is designed to extract the spectral features of superpixels, with which the spectral features can be acquired automatically. Furthermore, graph edges are introduced into Graph Attention Network (GAT) to acquire the local semantic feature of the graph. Moreover, inspired by the transformer network, we design a novel multi-scale receptive field GAT to extract the local-global adjacent node-features and edges-features. Finally, a graph attention network and a softMax function are utilized for multi-receptive feature fusion and pixel-label predicting. On Pavia University, Salinas, and Houston 2013 datasets, the overall accuracies (OAs) of our MRGAT are 71.76%, 82.61%, and 63.82%, respectively. Moreover, the performances with limited labeled samples indicates that the MRGAT contains superior adaptability. Compared with the competitive classifiers, MRGAT achieves high classification efficiency verified by training time comparison experiment.

A hybrid neural architecture search for hyperspectral image classification

Convolution neural network (CNN)is widely used in hyperspectral image (HSI) classification. However, the network architecture of CNNs is usually designed manually, which requires careful fine-tuning. Recently, many technologies for neural architecture search (NAS) have been proposed to automatically design networks, further improving the accuracy of HSI classification to a new level. This paper proposes a circular kernel convolution-β-decay regulation NAS-confident learning rate (CK-βNAS-CLR) framework to automatically design the neural network structure for HSI classification. First, this paper constructs a hybrid search space with 12 kinds of operation, which considers the difference between enhanced circular kernel convolution and square kernel convolution in feature acquisition, so as to improve the sensitivity of the network to hyperspectral information features. Then, the β-decay regulation scheme is introduced to enhance the robustness of differential architecture search (DARTS) and reduce the discretization differences in architecture search. Finally, we combined the confidence learning rate strategy to alleviate the problem of performance collapse. The experimental results on public HSI datasets (Indian Pines, Pavia University) show that the proposed NAS method achieves impressive classification performance and effectively improves classification accuracy.

Hyperspectral Image Classification with Deep CNN Using an Enhanced Elephant Herding Optimization for Updating Hyper-Parameters

Deep learning approaches based on convolutional neural networks (CNNs) have recently achieved success in computer vision, demonstrating significant superiority in the domain of image processing. For hyperspectral image (HSI) classification, convolutional neural networks are an efficient option. Hyperspectral image classification approaches are often based on spectral information. Convolutional neural networks are used for image classification in order to achieve greater performance. The complex computation in convolutional neural networks requires hyper-parameters that attain high accuracy outputs, and this process needs more computational time and effort. Following up on the proposed technique, a bio-inspired metaheuristic strategy based on an enhanced form of elephant herding optimization is proposed in this research paper. It allows one to automatically search for and target the suitable values of convolutional neural network hyper-parameters. To design an automatic system for hyperspectral image classification, the enhanced elephant herding optimization (EEHO) with the AdaBound optimizer is implemented for the tuning and updating of the hyper-parameters of convolutional neural networks (CNN–EEHO–AdaBound). The validation of the convolutional network hyper-parameters should produce a highly accurate response of high-accuracy outputs in order to achieve high-level accuracy in HSI classification, and this process takes a significant amount of processing time. The experiments are carried out on benchmark datasets (Indian Pines and Salinas) for evaluation. The proposed methodology outperforms state-of-the-art methods in a performance comparative analysis, with the findings proving its effectiveness. The results show the improved accuracy of HSI classification by optimising and tuning the hyper-parameters.

SS-TMNet: Spatial–Spectral Transformer Network with Multi-Scale Convolution for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a significant foundation for remote sensing image analysis, widely used in biology, aerospace, and other applications. Convolution neural networks (CNNs) and attention mechanisms have shown outstanding ability in HSI classification and have been widely studied in recent years. However, the existing CNN-based and attention mechanism-based methods cannot fully use spatial–spectral information, which is not conducive to further improving HSI classification accuracy. This paper proposes a new spatial–spectral Transformer network with multi-scale convolution (SS-TMNet), which can effectively extract local and global spatial–spectral information. SS-TMNet includes two key modules, i.e., multi-scale 3D convolution projection module (MSCP) and spatial–spectral attention module (SSAM). The MSCP uses multi-scale 3D convolutions with different depths to extract the fused spatial–spectral features. The spatial–spectral attention module includes three branches: height spatial attention, width spatial attention, and spectral attention, which can extract the fusion information of spatial and spectral features. The proposed SS-TMNet was tested on three widely used HSI datasets: Pavia University, IndianPines, and Houston2013. The experimental results show that the proposed SS-TMNet is superior to the existing methods.

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel. The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system. In this study, a unique pixel-based approach was designed to improve the crops' classification accuracy by using the edge-preserving features (EPF) and principal component analysis (PCA) in conjunction. The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI. In the second step, this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information. The resultant feature space (PCA-EPF) demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost. The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF. The classification performance evaluation was measured in terms of individual class accuracy, overall accuracy, average accuracy, and Cohen kappa factor. The proposed scheme achieved greater than 90 % results for all the performance evaluation metrics. The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range. The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.

Image classification of hyperspectral remote sensing using semi-supervised learning algorithm.

Hyperspectral images contain abundant spectral and spatial information of the surface of the earth, but there are more difficulties in processing, analyzing, and sample-labeling these hyperspectral images. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced to propose a sample labeling method based on neighborhood information and priority classifier discrimination. A new 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. 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 discrimination 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 classification method based on semi-supervised learning is proposed 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 have demonstrated that the proposed classification method is able to gain a higher classification accuracy, a stronger timeliness, and the generalization ability.

Lightweighted Hyperspectral Image Classification Network by Progressive Bi-Quantization

Convolutional neural network (CNN) has shown its powerful ability for hyperspectral image (HSI) classification, which however, is difficult to deploy on resource-limited or low-latency platforms due to its parameter and computation redundancy. Though binary neural network (BNN) has attracted attention for its extreme compressing and speeding up ability by binarizing both weights and activations, it has rarely been explored for HSI classification. In this study, we elaborately design a BNN with good performance for HSI classification task. Specifically, an adaptive gradient scale module is proposed to flexibly modify the gradient during training stage to better optimize the BNN and does not add any extra computation for inference. Furthermore, a curriculum learning-based progressive binarization strategy is utilized to improve the performance. Compared with the existing BNN works, our method can increase the HSI classification accuracy by a large margin while maintaining the compressing ratio. Abundant experiments on three datasets demonstrate the effectiveness of the proposed method.

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

Combining spectral and spatial features can increase the land-cover classification accuracy of hyperspectral images (HSI), but improvements are still needed to overcome problems remain in the extraction and use of spatial features, such as fixed extraction scale, loss of homogeneous region boundary and texture detail. Therefore, a novel classification frame is constructed herein. First, the first principal component of a Karhunen–Loeve transform of the HSI is taken as the guidance image to extract guide filter features of the other selected principal components under different scales and the appropriate multiscale range is automatically determined by calculating the interclass distance and intraclass distance based on the training samples. Second, the texture features, extracted by a local binary pattern, are used to emphasize the spatial information. Finally, logistic regression via variable splitting and augmented Lagrangian is used to obtain class labels from each scale image feature, and the majority voting algorithm is used to obtain the last class label of each pixel. Two standard HSI datasets, Indian Pines and Pavia University, are used to test this frame, and methods based on spectrum or spatial filtering (Type I) and methods combining spectral and spatial features (Type II) are used for comparison. The overall accuracy (OA) of the proposed frame is on average 13.76% and 4.77% higher than that of Type I and Type II, respectively, when 30 pixels per class are used as training samples. One dataset with a spatial resolution of 0.8 m, obtained by Panchromatic multispectral imaging spectrometer, is used for comparison with Type I, resulting in an average increase in OA of 7.05%, when 30 pixels per class are used as training samples. These results indicate the validity of the proposed frame.

Hyperspectral image classification method by coupling particle swarm optimization and multiple kernel support vector machine

There are two heterogeneous data types in hyperspectral image (HSI): rich spectral data and spatial information. Recent research has shown that the application of spectral–spatial information significantly improves HSI accuracy; thus, multiple-feature combination-based methods have been favored by researchers in the field of HSI classification. A multiple-feature combination approach, based on the particle swarm optimization (PSO) algorithm, is proposed for improving the accuracy of HSI classification. The proposed method couples a multiple kernel support vector machine (SVM) with a PSO algorithm to assign optimal weights to different kernels. Moreover, it also solves the problem of artificially selecting weights when learning multiple features by implementing adaptive weights on different datasets. In addition, it has fewer parameters and a shorter training time than deep learning methods, thus, the model is smaller and easier to train. The proposed method was tested on four datasets, containing two and three kernels. The experimental results show that our optimized method improves the classification accuracy; additionally, the kappa performance of the classification is also better.