Spectral-spatial Classification Of Hyperspectral Images Research Articles

Mayfly algorithm-based semi-supervised band selection with enhanced bitonic filter for spectral-spatial hyperspectral image classification

ABSTRACT Hyperspectral imaging effectively distinguishes land cover classes with discriminative spectral-spatial characteristics. However, hyperspectral image (HSI) classification encounters three significant challenges: the curse of dimensionality, the scarcity of training samples, and the necessity to incorporate spatial information within the classification process. The large number of redundant and irrelevant spectral bands in HSI can negatively impact the classification performance. Furthermore, the annotation process for training samples is expensive and time-consuming. To address these challenges, this work presents a novel semi-supervised approach for hyperspectral image dimensionality reduction using the Mayfly Algorithm (MA). The proposed method aims to preserve the most informative spectral bands while leveraging both labelled and unlabelled samples in the process. The MA algorithm effectively balances exploration and exploitation strategies, enhancing population diversity and mitigating challenges such as premature convergence and local optima. Additionally, the inclusion of structural variation and thresholding (SVT) in the Bitonic filter enables the incorporation of spatial features during the classification phase. This improves the separability of pixels belonging to different objects and leads to significant improvements in classification performance. Finally, two machine learning classifiers, namely Random Forest (RF) and Support Vector Machine (SVM), are applied at the pixel level for hyperspectral image classification. Extensive experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed approach compared to state-of-the-art algorithms like Harmony Search (HS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and more with an average improvement of 3.2%.

An Enhanced Semi-Supervised Support Vector Machine Algorithm for Spectral-Spatial Hyperspectral Image Classification

An Enhanced Semi-Supervised Support Vector Machine Algorithm for Spectral-Spatial Hyperspectral Image Classification

SSC-SFN: spectral-spatial non-local segment federated network for hyperspectral image classification with limited labeled samples

ABSTRACT Hyperspectral image (HSI) classification methods based on deep learning (DL) have performed well in numerous investigations. Although many modified superpixel-wise neural networks are utilized to enhance spatial information, their ability to mine spectral information in graph structures is insufficient. Moreover, single classifier approaches are unable to extract adequate spatial and spectral information simultaneously. For the classification of large-scale research areas, many works have relied on the use of a large number of labeled samples, leading to low efficiency and weak generalization. To address these issues, an effective spectral-spatial HSI classification approach based on spectral-spatial non-local segment federated network (SSC-SFN) was developed in this study. In this framework, deconvolution is employed to recover the data size, while the lost spatial information is replaced by up-pooling. The spectral dimensional features are updated through the generation of non-Euclidean graph structures and the non-local segment smoothing technique. The convolutional neural network and graph convolutional network techniques are coupled to exploit the available spectral and spatial structure information fully. Extensive experimental results obtained using four public benchmark datasets show that the classification accuracy of SSC-SFN can exceed 90% for large-scale HSIs with limited samples.

Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images

Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images

Weighted residual self-attention graph-based transformer for spectral–spatial hyperspectral image classification

ABSTRACT Recently, deep learning for hyperspectral image classification has been successfully applied, and some convolutional neural network (CNN)-based models already achieved attractive classification results. Since hyperspectral data is a spectral-spatial cube data that can generally be considered as sequential data along with the spectral dimension, CNN models perform poorly on such a sequential data. Unlike convolutional neural networks (CNNs) that mainly concern with local relationship models in images, transformer has been shown to be a powerful structure for qualifying sequential data. In the SA (self-attention) module of ViT, each token is updated through aggregating all token’s features based on the self-attention graph. Through this, tokens can exchange information sufficiently among each other which provides a powerful representation capability. However, as the layers become deeper, the transformer model suffers from network degradation. Therefore, in order to improve the layer-to-layer information exchange and alleviate the network degradation problem, we propose a Weighted Residual Self-attention Graph-based Transformer (RSAGformer) model for hyperspectral image classification with respect to the self-attention mechanism. It effectively solves the network degradation problem of deep transformer model by fusing the self-attention information between adjacent layers and extracts the information of data effectively. Extensive experiment evaluation with six public hyperspectral datasets shows that the RSAGformer yields competitive results for classification.

Machine Learning and Deep Learning Techniques for Spectral Spatial Classification of Hyperspectral Images: A Comprehensive Survey

The growth of Hyperspectral Image (HSI) analysis is due to technology advancements that enable cameras to collect hundreds of continuous spectral information of each pixel in an image. HSI classification is challenging due to the large number of redundant spectral bands, limited training samples and non-linear relationship between the collected spatial position and the spectral bands. Our survey highlights recent research in HSI classification using traditional Machine Learning techniques like kernel-based learning, Support Vector Machines, Dimension Reduction and Transform-based techniques. Our study also digs into Deep Learning (DL) techniques that involve the usage of Autoencoders, 1D, 2D and 3D-Convolutional Neural Networks to classify HSI. From the comparison, it is observed that DL-based classification techniques outperform ML-based techniques. It has also been observed that spectral-spatial HSI classification outperforms pixel-by-pixel classification because it incorporates spectral signatures and spatial domain information. The performance of ML and DL-based classification techniques has been reviewed on commonly used land cover datasets like Indian Pines, Salinas valley and Pavia University.

Spectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms

Spectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms

Cascaded Convolution-Based Transformer With Densely Connected Mechanism for Spectral–Spatial Hyperspectral Image Classification

Hyperspectral image (HSI) classification attempts to classify each pixel, which is an important means of obtaining land–cover knowledge. Hyperspectral images are cubic data with spectral–spatial knowledge and can generally be considered as sequential data alongside spectral dimension. Unlike convolutional neural networks (CNNs), which mainly focus on local relationship models in images, transformers have been shown to be a powerful structure for qualifying sequence data. However, it lacks the excellent ability of CNNs in establishing local relationships in images and cannot perform good generalization in case of insufficient data. In addition, the gradient disappearance problem hinders the convergence stability of deep learning networks as the layers get deeper. To address these problems, we propose a Cascaded Convolution-based Transformer with Densely Connected Mechanism (CDCformer) for hyperspectral image classification. First, we propose a cascaded convolution feature tokenization to extract spectral–spatial information, which will introduce some inductive bias properties of CNN into the transformer. In addition, we design a simple and effective densely connected transformer to enhance feature propagation and transfer memorable information from shallow to deep layers. It efficiently improves the performance of the transformer and extracts more discriminative spectral–spatial features from the HSI. Extensive experimental evaluation of three public hyperspectral data sets shows that CDCformer achieves competitive classification results.

Spectral-spatial classification of hyperspectral images based on non-linear principal component analysis and deep learning models

ABSTRACT Hyperspectral sensors, due to the acquisition of a large number of spectral bands, always have particular importance in monitoring the phenomena of the earth’s surface. The classification of hyperspectral images is the most crucial method of processing hyperspectral data, and many efforts have been made to increase its accuracy. In recent years, convolutional neural networks (CNNs) and spatial features have been a big part of how well hyperspectral images can be classified. CNNs, due to the automatic generation of features and reduction of parameters, compared to multilayer perceptron neural networks by sharing parameters in each layer, have received much attention from researchers in the field of pattern recognition. In previous research, much attention has not been paid to the simultaneous use of spatial feature extraction methods in CNNs. For this reason, in this paper, a new CNNs architecture has been introduced to classify hyperspectral images, which uses the spectral-spatial vector obtained from the morphological profiles method as the network’s input. Morphological profiles, which include a set of opening and closing filters, are generally applied to the components of the image that contain the most information. In this research, non-linear principal component analysis (non-linear PCA) implemented by neural networks was used to summarize hyperspectral image information. Therefore, the main innovation of this paper is to provide a three-stage framework for applying deep learning. The first step is to reduce the dimensions of the hyperspectral image using non-linear PCA, the second stage is to extract spatial features using the morphological profiles method, and the third step is to prepare the inputs and design the CNNs architecture. The proposed method was evaluated on three benchmark hyperspectral images of Pavia, Berlin and Telops. The results of the experiments show the superiority of the proposed method by 13, 11 and 17% in the overall accuracy parameter compared to the support vector machines (SVM) classification algorithm in the Pavia, Berlin and Telops images, respectively.

A lightweight 3D-2D convolutional neural network for spectral-spatial classification of hyperspectral images

Hyperspectral Image (HSI) is usually composed of hundreds of capturing wavelength bands, which not only increase the size of the HSI rapidly but also impose various obstacles in classifying the objects accurately. Moreover, the traditional machine learning schemes utilize only the spectral features for HSI classification, which, therefore, neglect the spatial features that have a significant impact on the classification improvement. To address the aforementioned issues, in this paper, we propose to employ the principal component analysis (PCA), the baseline feature extraction method, and a thoughtfully designed stacked autoencoder, a deep learning-based feature extraction approach, for reducing the high dimensionality of the HSI and then propose a novel lightweight 3D-2D convolutional neural network (CNN) framework to concurrently exploit both spatial and spectral features from the dimensionality-reduced HSI for classification. In particular, PCA and stacked autoencoder are applied to reduce the high dimensionality of the original HSI and then the proposed 3D-2D CNN provides a combination of 3D and 2D convolution operations to extract the subtle spatial and spectral features for efficient classification. We well-adjust the proposed 3D-2D CNN architecture, and perform extensive experiments on three benchmark HSI datasets and compare our approach with the state-of-the-art classical and deep learning methods. Experimental results illustrate that we have achieved an overall accuracy of 99.73%, 99.90%, and 99.32% on Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively, which outperform the classical machine learning and independent 2D and 3D CNN-based state-of-the-art methods.

Spectral-spatial hyperspectral image classification based on capsule network with limited training samples

ABSTRACT For hyperspectral images, the classification problem of limited training samples is an enormous challenge, and the lack of training samples is an essential factor that affects the classification accuracy. Making full use of the rich spectral and spatial information contained in hyperspectral images can achieve a high-precision classification of hyperspectral images with limited training samples. This paper proposed a new spectral-spatial feature extraction method and constructed a new classification framework for limited training samples. In our work, deep reinforcement learning is used to process the spectral features of hyperspectral images, and then the selected bands with the most information are used to extract the spatial features by extended morphological profiles. Finally, the hyperspectral image after feature extraction is classified by the capsule network, which is more effective at exploiting the relationships between hyperspectral imaging features in the spectral-spatial domain. We conducted classification experiments on five well-known hyperspectral image data sets. The experimental results reveal that the proposed method can better extract the spectral and spatial features in hyperspectral images, achieve higher classification accuracy with limited training samples, and reduce computational and time complexity.

On Spectral-Spatial Classification of Hyperspectral Images Using Image Denoising and Enhancement Techniques, Wavelet Transforms and Controlled Data Set Partitioning

Obtaining relevant classification results for hyperspectral images depends on the quality of the data and the proposed selection of the samples and descriptors for the training and testing phases. We propose a hyperspectral image classification machine learning framework based on image processing techniques for denoising and enhancement and a parallel approach for the feature extraction step. This parallel approach is designed to extract the features by employing the wavelet transform in the spectral domain, and by using Local Binary Patterns to capture the texture-like information linked to the geometry of the scene in the spatial domain. The spectral and spatial features are concatenated for a Support Vector Machine-based supervised classifier. For the experimental validation, we propose a controlled sampling approach that ensures the independence of the selected samples for the training data set, respectively the testing data set, offering unbiased performance results. We argue that a random selection applied on the hyperspectral dataset to separate the samples for the learning and testing phases can cause overlapping between the two datasets, leading to biased classification results. The proposed approach, with the controlled sampling strategy, tested on three public datasets, Indian Pines, Salinas and Pavia University, provides good performance results.

Spectral-Spatial Classification of Hyperspectral Image Using Improved Functional Principal Component Analysis

The functional principal component analysis (FPCA) method can effectively solve the problems of the high dimensionality of data, large information redundancy, and noise interference in hyperspectral image (HSI) classification. However, this unsupervised FPCA cannot make full use of the label information of training samples or spatial information, so that it is impossible to obtain satisfactory classification results. In this letter, a set of improved FPCA methods for HSI classification are proposed. First, the B-spline basis system is used to establish the functional data fitting model, which can convert discrete spectral information into continuous spectral curves and lay the foundation for functional feature extraction. Second, a supervised FPCA (SFPCA) method is built for extracting more effective functional features by making full use of the label information of training samples. Furthermore, to overcome the lack of training samples, two semisupervised FPCA (SSFPCA) methods are proposed for extracting more discriminative functional features and improving the classification accuracies. Finally, we perform the local mean filtering method on the HSI in order to extract the spatial information for each pixel, and then design spectral-spatial classification frameworks based on improved FPCA. Experiments on the commonly used HSI dataset show that improved FPCA can achieve higher classification accuracies than FPCA, and the proposed functional spectral-spatial classification frameworks can greatly improve classification accuracies.

Spectral–Spatial Hyperspectral Image Classification Based on Multiple Views and Multigraphs Fusion With Few Labeled Samples

The issue of limited labeled samples is still grave in hyperspectral image classification. Semisupervised learning (SSL) utilizing both labeled and unlabeled samples promotes a solution to this issue. However, it has been found that the performance of single SSL is frail when the labeled samples is limited. To tackle this problem, we propose 3D-Gabor and multiple graphs semisupervised framework (3DG-MGSF). The whole framework is tripartite, including multi-views generation and selection, multiple graphs-based label propagation, and double layers classification fusion process. Specifically, a number of 3D-Gabor filters with various directions and frequencies are employed to generate multiple views. Afterwards, a double multi-views selection procedure is applied to ensure the sufficiency and diversity of multiple views. Subsequently, it is time for the multiple graphs-based label propagation to put on its pump. Moreover, spatial and spectral classifications are combined based on the weighted re-fusion algorithm to obtain the final classification. Experimental results illustrate numerically and visually the significantly superior performance of proposed algorithm compared with four state-of-the-art algorithms with few labeled samples.

Proxy-Based Deep Learning Framework for Spectral–Spatial Hyperspectral Image Classification: Efficient and Robust

Deep convolutional networks have been extensively deployed in hyperspectral image (HSI) classification. Reaching for high accuracy, the existing deep-learning-based methods commonly deepen or widen their networks for better performance, which brings higher computational complexity and the risk of overfitting. Although the introduction of the residual module and batch-normalization reduces the generalization degradation in complex networks, the mainstream methods still suffer from low robustness to the noise. To tackle these issues, a compact proxy-based deep learning framework is proposed to perform highly accurate HSI classification with superb efficiency and robustness. In this article: 1) novel deep proxies are integrated to replace the dense classifier layers in conventional networks, which represents specific classes in deep embedding space and enables fast and reliable convergence; 2) the proxy-based feature embedding is studied in distance metric and similarity metric, and compatible dual-metric loss functions are designed for further optimized embedding distribution, which leads to more robust generalization; and 3) state-of-the-art performance and robustness are demonstrated by the proposed framework on mainstream HSI data sets with the minimal network scale and time complexity.

Generative Adversarial Minority Oversampling for Spectral–Spatial Hyperspectral Image Classification

Recently, convolutional neural networks (CNNs) have exhibited commendable performance for hyperspectral image (HSI) classification. Generally, an important number of samples are needed for each class to properly train CNNs. However, existing HSI data sets suffer from a significant class imbalance problem, where many classes do not have enough samples to characterize the spectral information. The performance of existing CNN models is biased toward the majority classes, which possess more samples for the training. This article addresses this issue of imbalanced data in HSI classification. In particular, a new <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D-HyperGAMO</monospace> model is proposed, which uses generative adversarial minority oversampling. The proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D-HyperGAMO</monospace> automatically generates more samples for minority classes at training time, using the existing samples of that class. The samples are generated in the form of a 3-D hyperspectral patch. A different classifier from the generator and the discriminator is used in the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D-HyperGAMO</monospace> model, which is trained using both original and generated samples to determine the classes of newly generated samples to which they actually belong. The generated data are combined classwise with the original training data set to learn the network parameters of the class. Finally, the trained 3-D classifier network validates the performance of the model using the test set. Four benchmark HSI data sets, namely, Indian Pines (IP), Kennedy Space Center (KSC), University of Pavia (UP), and Botswana (BW), have been considered in our experiments. The proposed model shows outstanding data generation ability during the training, which significantly improves the classification performance over the considered data sets. The source code is available publicly at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mhaut/3D-HyperGAMO</uri> .

Hierarchical broad learning system for hyperspectral image classification

A new spectral-spatial hyperspectral image (HSI) classification method called hierarchical broad learning system (HBLS) has been proposed in this paper. Specifically, it combines wavelet, broad learning system (BLS) and Gabor filters into a hierarchical structure. First of all, wavelet is used to reduce the observation noise of HSIs. Then BLS is adopted to acquire a set of pixelwise probability maps from the input data, and Gabor filters are used to explore spatial information by refining these probability maps. These two operations (BLS and Gabor filtering) are alternated to form a hierarchical architecture. And the discriminative spectral-spatial features can be extracted at each layer of the hierarchical architecture. Finally, the spectral-spatial features are fed into the standard BLS for classification. Experimental results on three widely used HSIs reveal that HBLS outperforms some state-of-the-art methods in terms of classification accuracy and sample complexity.

Spectral-spatial hyperspectral image classification with dual spatial ensemble learning

ABSTRACT The spatial information in hyperspectral images (HSIs) plays a vital role in supervised classification tasks. However, although the introduction to spatial information can effectively improve the classification accuracy for representation-based supervised classifiers, it is difficult to avoid the expensive cost of time consumption. To solve this problem, a dual spatial ensemble learning (DSEL) method is proposed in this letter, which consists of the following key technologies: 1) first, the principal component analysis is applied to original hyperspectral data to extract the first three principal components (PCs). Then, the first three PCs are utilized as a base image of an over-segmentation method to cluster the HSI into many shape-adaptive superpixels. Finally, the raw HSI is guided by superpixels to construct new feature data with spatial information. 2) A representation-based classifier is used on the feature data to achieve initial classification results. Specifically, the results are converted into probability based on mathematical statistics. 3) A random walk algorithm is exploited to optimize the probabilities according to the spatial relationship between pixels. Experimental results on the Indian Pines and University of Pavia datasets demonstrated that the proposed DSEL method achieves superior performance compared with several competitive methods.

Iterative Training Sampling Coupled With Active Learning for Semisupervised Spectral–Spatial Hyperspectral Image Classification

Training sample selection is a great challenge for hyperspectral image classification (HSIC), specifically when only a very limited number of labeled data samples are available for training. Two recently developed concepts for training sample selection are of particular interest. One is active learning (AL), which augments labeled training samples by including unlabeled data samples as new training samples. The other is iterative training sampling (ITS), which expands data cubes by including additional spatial classification information into the training samples. This article combines AL and ITS simultaneously to derive a joint ITS–AL spectral–spatial (SS) classification approach, to be called ITS augmentation by AL spectral–spatial classification, referred to as ITSA-AL-SS, which can improve SS classification using either AL or ITS alone. The novel idea of ITSA-AL-SS is to take advantage of ITS to expand data cubes by incorporating additional spatial classification information iteratively into the AL-augmented unlabeled data samples to update the current training samples iteration by iteration in one-shot operation. As expected, ITSA-AL-SS is benefited from both ITS and AL to not only further improve classification accuracy but also reduce classification inconsistency.

Hyperspectral Image Classification Based on Sparse Superpixel Graph

Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (&gt;97.85%) and rapid implementation (&lt;10 s). This clearly favors the application of the proposed method in practice.