Hyperspectral Remote Sensing Image Classification Research Articles

Hyperspectral Image Classification Based on Mutually Guided Image Filtering

Hyperspectral remote sensing images (HSIs) have both spectral and spatial characteristics. The adept exploitation of these attributes is central to enhancing the classification accuracy of HSIs. In order to effectively utilize spatial and spectral features to classify HSIs, this paper proposes a method for the spatial feature extraction of HSIs based on a mutually guided image filter (muGIF) and combined with the band-distance-grouped principal component. Firstly, aiming at the problem that previously guided image filtering cannot effectively deal with the inconsistent information structure between the guided and target information, a method for extracting spatial features using muGIF is proposed. Then, aiming at the problem of the information loss caused by a single principal component as a guided image in the traditional GIF-based spatial–spectral classification, a spatial feature-extraction framework based on the band-distance-grouped principal component is proposed. The method groups the bands according to the band distance and extracts the principal components of each set of band subsets as the guide map of the current band subset to filter the HSIs. A deep convolutional neural network model and a generative adversarial network model for the filtered HSIs are constructed and then trained using samples for HSIs’ spatial–spectral classification. Experiments show that compared with the traditional methods and several popular spatial–spectral HSI classification methods based on a filter, the proposed methods based on muGIF can effectively extract the spatial–spectral features and improve the classification accuracy of HSIs.

Hyperspectral remote sensing image classification based on multidirectional adaptive aware network

Hyperspectral remote sensing image classification based on multidirectional adaptive aware network

Retracted: I3D: An Improved Three-Dimensional CNN Model on Hyperspectral Remote Sensing Image Classification

Retracted: I3D: An Improved Three-Dimensional CNN Model on Hyperspectral Remote Sensing Image Classification

Enhancing hyperspectral remote sensing image classification using robust learning technique

Advanced sensor tech integrates into diverse applications, including remote sensing, robotics, and IoT. Combining artificial intelligence (AI) with sensors enhances their capabilities, creating smart sensors, revolutionizing remote sensing and Internet of Things (IoT). This synergy forms a potent technology in the field. This study carries out a comprehensive analysis of the progress made in Hyperspectral sensors and AI-based classification techniques that are employed in remote sensing fields that utilize hyperspectral images. The classification of images obtained from Hyperspectral Sensors (HSS) has emerged as a prominent research subject within the domain of remote sensing. HSS offer a wealth of information across numerous spectral bands, supporting diverse applications such as land cover classification, environmental monitoring, agricultural assessment, change detection, and more. However, the abundance of data present in HSS also poses the challenge called the curse of dimensionality. The reduction of data dimensionality is crucial before applying any machine learning model to achieve optimal results. The present study introduces a new hybrid strategy combining the Back-Propagation algorithm with a variable adaptive momentum (BPVAM) and principal component analysis (PCA) for the purpose of classifying hyperspectral images. PCA is first applied to obtain an optimal set of discriminative features by eliminating highly correlated and redundant features. These features are then fed into the BPVAM model for classification. The addition of the momentum term in the weight update equation of the backpropagation algorithm helped achieve faster convergence with high accuracy. The proposed model was subjected to evaluation through experiments conducted on two benchmark datasets. These results indicated that the hybrid model based on BPVAM with PCA is an efficient technique for HSS classification.

Information Leakage in Deep Learning-Based Hyperspectral Image Classification: A Survey

In deep learning-based hyperspectral remote sensing image classification tasks, random sampling strategies are typically used to train model parameters for testing and evaluation. However, this approach leads to strong spatial autocorrelation between the training set samples and the surrounding test set samples, and some unlabeled test set data directly participate in the training of the network. This leaked information makes the model overly optimistic. Models trained under these conditions tend to overfit to a single dataset, which limits the range of practical applications. This paper analyzes the causes and effects of information leakage and summarizes the methods from existing models to mitigate the effects of information leakage. Specifically, this paper states the main issues in this area, where the issue of information leakage is addressed in detail. Second, some algorithms and related models used to mitigate information leakage are categorized, including reducing the number of training samples, using spatially disjoint sampling strategies, few-shot learning, and unsupervised learning. These models and methods are classified according to the sample-related phase and the feature extraction phase. Finally, several representative hyperspectral image classification models experiments are conducted on the common datasets and their effectiveness in mitigating information leakage is analyzed.

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.

Three-dimensional Softmax Mechanism Guided Bidirectional GRU Networks for Hyperspectral Remote Sensing Image Classification

Hyperspectral data is a valuable source of both spectral and spatial information. However, to enhance the classification accuracy of hyperspectral image features, it is crucial to capture the spatial spectral features of image elements. The recent years have witnessed the potentials of deep learning methods have shown great promise in the hyperspectral image classification due to their ability to model complex structures and extract multiple features in an end-to-end fashion. Since hyperspectral images can be viewed as sequential data, we propose a novel three-dimensional Softmax mechanism-guided bidirectional GRU network (TDS-BiGRU) for HSI classification. By utilizing a bidirectional GRU to process the sequence data, our method can significantly reduce the processing time. Furthermore, the proposed three-dimensional Softmax mechanism leverages three branches to capture cross-latitude interactions and calculate Softmax weights, which enables us to obtain deeper features with greater discriminative power. The experimental results demonstrate that the proposed method outperforms several prevalent algorithms on four hyperspectral remote sensing datasets. Additionally, we conduct thorough comparisons and ablation tests, which further confirm the effectiveness of our approach.

Hyperspectral Remote Sensing Images Feature Extraction Based on Spectral Fractional Differentiation

To extract effective features for the terrain classification of hyperspectral remote-sensing images (HRSIs), a spectral fractional-differentiation (SFD) feature of HRSIs is presented, and a criterion for selecting the fractional-differentiation order is also proposed based on maximizing data separability. The minimum distance (MD) classifier, support vector machine (SVM) classifier, K-nearest neighbor (K-NN) classifier, and logistic regression (LR) classifier are used to verify the effectiveness of the proposed SFD feature, respectively. The obtained SFD feature is sent to the full connected network (FCN) and 1-dimensionality convolutional neural network (1DCNN) for deep-feature extraction and classification, and the SFD-Spa feature cube containing spatial information is sent to the 3-dimensionality convolutional neural network (3DCNN) for deep-feature extraction and classification. The SFD-Spa feature after performing the principal component analysis (PCA) on spectral pixels is directly connected with the first principal component of the original data and sent to 3DCNNPCA and hybrid spectral net (HybridSN) models to extract deep features. Experiments on four real HRSIs using four traditional classifiers and five network models have shown that the extracted SFD feature can effectively improve the accuracy of terrain classification, and sending SFD feature to deep-learning environments can further improve the accuracy of terrain classification for HRSIs, especially in the case of small-size training samples.

A hybrid classification method with dual-channel CNN and KELM for hyperspectral remote sensing images

ABSTRACT In this paper, a hybrid classification method based on dual-channel convolutional neural network (DC-CNN) and kernel extreme learning machine (KELM), namely PLDC-KELM, is proposed to improve the spatial-spectral feature extraction ability and hyperspectral remote sensing image (HRSI) classification accuracy. In the proposed PLDC-KELM, principal component analysis (PCA) is employed to reduce the dimensionality of original HRSI, and local binary pattern (LBP) is utilized to extract spatial features from the data after dimensionality reduction, and the one-dimensional convolutional neural network (1D-CNN) model is constructed to extract deep spatial features from the dimensionality-reduced spatial features. The secure connection layers of two 1D-CNN models are connected to fuse deep spectral and spatial features. Finally, the fused features are input into KELM in order to realize an HRSI classification method. Indian Pines data, Pavia University data, and Salinas data are used to prove the effectiveness of the PLDC-KELM. The experimental results show that the PLDC-KELM method can achieve classification accuracy of 99.95%, 99.98%, and 99.97%, respectively. It is an effective method for hyperspectral remote sensing image classification.

Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning

Remote sensing image (RSI) classifier roles a vital play in earth observation technology utilizing Remote sensing (RS) data are extremely exploited from both military and civil fields. More recently, as novel DL approaches develop, techniques for RSI classifiers with DL have attained important breakthroughs, providing a new opportunity for the research and development of RSI classifiers. This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification (ISMOGCN-HRSC) model. The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs. In the presented ISMOGCN-HRSC model, the synergic deep learning (SDL) model is exploited to produce feature vectors. The GCN model is utilized for image classification purposes to identify the proper class labels of the RSIs. The ISMO algorithm is used to enhance the classification efficiency of the GCN method, which is derived by integrating chaotic concepts into the SMO algorithm. The experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset.

Improved Three-Dimensional Inception Networks for Hyperspectral Remote Sensing Image Classification

Convolutional neural networks (CNNs) have been applied to the categorization of hyperspectral images. Typically, CNNs employ fixed convolution kernel sizes. This may be complicated by the high-dimensional features and the spatial-spectral features variability of hyperspectral data. In such situation, dealing with various dataset types flexibly is hampered by the fixed kernel size. Therefore, we chose a parallel design, an upgraded three-dimensional (3D) inception network, for hyperspectral image classification. Additionally, the adaptive band selection of hyperspectral remote sensing data employs dimensionality reduction based on interactive information entropy, allowing the selection of low-redundant bands with more abundant and discriminative information. Experimental results based on the publicly available hyperspectral datasets demonstrated that the enhanced 3D inception network can perform well with limited training samples. Moreover, the combinations of different dimensional convolutions in the 3D-2D and 3D-2D-1D inception networks achieved comparable classification results. According to the experimental findings, the suggested networks are generalised classification models with high classification rates.

A Multi-branch Feature Fusion Model Based on Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification

A Multi-branch Feature Fusion Model Based on Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification

Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification

Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification

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.

Towards explainable AI for hyperspectral image classification in Edge Computing environments

Several Remote Sensing applications in Hyperspectral Imaging rely on Artificial Intelligence techniques, particularly Deep Neural Networks, which often outperform other algorithms. However, despite their effectiveness, these techniques are considered opaque for the non-linearity of the underlying model, making a conscious results interpretation difficult even for experts in the application field. The present work aims to describe the results of our experiments toward Explainable Artificial Intelligence techniques for hyperspectral remote sensing image classification in Edge Computing environments. The proposed technique extends the traditional 2D Gradient-weighted Class Activation Mapping (Grad-CAM) to 3D Convolutional Neural Networks. Moreover, we use spectral-cumulative Grad-CAM and class probability as complementary methods. The experimental results confirm that the observer can interpret the choices made within neural network layers by visualizing the activation volumes provided by the proposed method.

Wide and Deep Fourier Neural Network for Hyperspectral Remote Sensing Image Classification

Hyperspectral remote sensing image (HSI) classification is very useful in different applications, and recently, deep learning has been applied for HSI classification successfully. However, the number of training samples is usually limited, causing difficulty in use of very deep learning models. We propose a wide and deep Fourier network to learn features efficiently by using pruned features extracted in the frequency domain. It is composed of multiple wide Fourier layers to extract hierarchical features layer-by-layer efficiently. Each wide Fourier layer includes a large number of Fourier transforms to extract features in the frequency domain from a local spatial area using sliding windows with given strides.These extracted features are pruned to retain important features and reduce computations. The weights in the final fully connected layers are computed using least squares. The transform amplitudes are used for nonlinear processing with pruned features. The proposed method was evaluated with HSI datasets including Pavia University, KSC, and Salinas datasets. The overall accuracies (OAs) of the proposed method can reach 99.77%, 99.97%, and 99.95%, respectively. The average accuracies (AAs) can achieve 99.55%, 99.95%, and 99.95%, respectively. The Kappa coefficients are as high as 99.69%, 99.96%, and 99.94%, respectively. The experimental results show that the proposed method achieved excellent performance among other compared methods. The proposed method can be used for applications including classification, and image segmentation tasks, and has the ability to be implemented with lightweight embedded computing platforms. The future work is to improve the method to make it available for use in applications including object detection, time serial data prediction, and fast implementation.

Hyperspectral Remote Sensing Image Classification Based on Partitioned Random Projection Algorithm

Dimensionality reduction based on random projection (RP) includes two problems, namely, the dimensionality is limited by the data size and the class separability of the dimensionality reduction results is unstable due to the randomly generated projection matrix. These problems make the RP algorithm unsuitable for large-size hyperspectral image (HSI) classification. To solve these problems, this paper presents a new partitioned RP (PRP) algorithm and proves its rationality in theory. First, a large-size HSI is evenly divided into multiple small-size sub-HSIs. Afterwards, the projection matrix that maximizes the class separability is selected from multiple samplings in which the class dissimilarity measurement is defined as large inter-class distance and small intra-class variance. By using the same projection matrix, each small-size sub-HSI is projected to generate a low dimensional sub-HSI, thereby generating a low dimensional HSI. Next, the minimum distance (MD) classifier is utilized to classify the low dimensional HSI obtained by the PRP algorithm. Finally, four real HSIs are used for experiments, and three of the most popular classification algorithms based on RP are selected as comparison algorithms to validate the effectiveness of the proposed algorithm. The classification performance is evaluated with the kappa coefficient, overall accuracy (OA), average accuracy (AA), average precision rate (APR), and running time. Experimental results indicate that the proposed algorithm can obtain reliable classification results in a very short time.

Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning

Hyperspectral remote sensing image classification has been widely employed for numerous applications, such as environmental monitoring, agriculture, and mineralogy. During such classification, the number of training samples in each class often varies significantly. This imbalance in the dataset is often not identified because most classifiers are designed under a balanced dataset assumption, which can distort the minority classes or even treat them as noise. This may lead to biased and inaccurate classification results. This issue can be alleviated by applying preprocessing techniques that enable a uniform distribution of the imbalanced data for further classification. However, it is difficult to add new natural features to a training model by artificial combination of samples by using existing preprocessing techniques. For minority classes with sparse samples, the addition of sufficient natural features can effectively alleviate bias and improve the generalization. For such an imbalanced problem, semi-supervised learning is a creative solution that utilizes the rich natural features of unlabeled data, which can be collected at a low cost in the remote sensing classification. In this paper, we propose a novel semi-supervised learning-based preprocessing solution called NearPseudo. In NearPseudo, pseudo-labels are created by the initialization classifier and added to minority classes with the corresponding unlabeled samples. Simultaneously, to increase reliability and reduce the misclassification cost of pseudo-labels, we created a feedback mechanism based on a consistency check to effectively select the unlabeled data and its pseudo-labels. Experiments were conducted on a state-of-the-art representative hyperspectral dataset to verify the proposed method. The experimental results demonstrate that NearPseudo can achieve better classification accuracy than other common processing methods. Furthermore, it can be flexibly applied to most typical classifiers to improve their classification accuracy. With the intervention of NearPseudo, the accuracy of random forest, k-nearest neighbors, logistic regression, and classification and regression tree increased by 1.8%, 4.0%, 6.4%, and 3.7%, respectively. This study addresses research a gap to solve the imbalanced data-based limitations in hyperspectral image classification.

Multiscale Feature Aggregation Capsule Neural Network for Hyperspectral Remote Sensing Image Classification

Models based on capsule neural network (CapsNet), a novel deep learning method, have recently made great achievements in hyperspectral remote sensing image (HSI) classification due to their excellent ability to implicitly model the spatial relationship knowledge embedded in HSIs. However, the number of labeled samples is a common bottleneck in HSI classification, limiting the performance of these deep learning models. To alleviate the problem of limited labeled samples and further explore the potential of CapsNet in the HSI classification field, this study proposes a multiscale feature aggregation capsule neural network (MS-CapsNet) based on CapsNet via the implementation of two branches that simultaneously extract spectral, local spatial, and global spatial features to integrate multiscale features and improve model robustness. Furthermore, because deep features are generally more discriminative than shallow features, two kinds of capsule residual (CapsRES) blocks based on 3D convolutional capsule (3D-ConvCaps) layers and residual connections are proposed to increase the depth of the network and solve the limited labeled sample problem in HSI classification. Moreover, a squeeze-and-excitation (SE) block is introduced in the shallow layers of MS-CapsNet to enhance its feature extraction ability. In addition, a reasonable initialization strategy that transfers parameters from two well-designed, pretrained deep convolutional capsule networks is introduced to help the model find a good set of initializing weight parameters and further improve the HSI classification accuracy of MS-CapsNet. Experimental results on four widely used HSI datasets demonstrate that the proposed method can provide results comparable to those of state-of-the-art methods.

Hyperspectral Data Classification Algorithm considering Spatial Texture Features

As a cutting-edge technology, hyperspectral remote sensing has been widely applied in many fields, including agricultural production, mineral identification, target detection, disaster warning, military reconnaissance, and urban planning. The collected hyperspectral data have high spectral resolution and spatial resolution and are characterized by a large amount of information, redundancy, and high dimension. At the same time, there is a strong correlation between the bands. Therefore, hyperspectral data not only provides rich information but also brings great challenges for subsequent processing. Hyperspectral image classification is a hot issue in remote sensing information processing. Traditional hyperspectral remote sensing image classification methods only use the spectral features of the image without considering the spatial features of each pixel in the hyperspectral remote sensing image. In this paper, a hyperspectral image classification method is proposed not only considering spectral features but also considering texture features. This method jointly considers both these features. Firstly, six texture features contributing a lot to each pixel of hyperspectral remote sensing image are extracted by using a gray level cooccurrence matrix, and then, the spectral features of each pixel in neighbor are combined to form the texture-spectral features. Finally, the classification experiment of the Indian Pines and Pavia University scene is carried out based on a support vector machine and extreme random tree algorithm, and the obtained results show that the proposed method achieves higher classification performance than the traditional method.