Hyperspectral Image Classification Method Research Articles

Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review

Hyperspectral imaging has been used in a variety of geological applications since its advent in the 1970s. In the last few decades, different techniques have been developed by geologists to analyze hyperspectral data in order to quantitatively extract geological information from the high-spectral-resolution remote sensing images. We attempt to review and update various steps of the techniques used in geological information extraction, such as lithological and mineralogical mapping, ore exploration, and environmental geology. The steps include atmospheric correction, dimensionality processing, endmember extraction, and image classification. It is identified that per-pixel and subpixel image classifiers can generate accurate alteration mineral maps. Producing geological maps of different surface materials including minerals and rocks is one of the most important geological applications. The hyperspectral images classification methods demonstrate the potential for being used as a main tool in the mining industry and environmental geology. To exemplify the potential, we also include a few case studies of different geological applications.

A 3D Cascaded Spectral–Spatial Element Attention Network for Hyperspectral Image Classification

Most traditional hyperspectral image (HSI) classification methods relied on hand-crafted or shallow-based descriptors, which limits their applicability and performance. Recently, deep learning has gradually become the mainstream method of HSI classification, because it can automatically extract deep abstract features for classification. However, it remains a challenge to learn more meaningful features for HSI classification from a small training sample set. In this paper, a 3D cascaded spectral–spatial element attention network (3D-CSSEAN) is proposed to solve this issue. The 3D-CSSEAN integrates the spectral–spatial feature extraction and attention area extraction for HSI classification. Two element attention modules in the 3D-CSSEAN enable the deep network to focus on primary spectral features and meaningful spatial features. All attention modules are implemented though several simple activation operations and elementwise multiplication operations. In this way, the training parameters of the network are not added too much, which also makes the network structure suitable for small sample learning. The adopted module cascading pattern not only reduces the computational burden in the deep network but can also be easily operated via plug–expand–play. Experimental results on three public data sets show that the proposed 3D-CSSEAN achieved comparable performance with the state-of-the-art methods.

Spatial-Spectral Network for Hyperspectral Image Classification: A 3-D CNN and Bi-LSTM Framework

Recently, deep learning methods based on the combination of spatial and spectral features have been successfully applied in hyperspectral image (HSI) classification. To improve the utilization of the spatial and spectral information from the HSI, this paper proposes a unified network framework using a three-dimensional convolutional neural network (3-D CNN) and a band grouping-based bidirectional long short-term memory (Bi-LSTM) network for HSI classification. In the framework, extracting spectral features is regarded as a procedure of processing sequence data, and the Bi-LSTM network acts as the spectral feature extractor of the unified network to fully exploit the close relationships between spectral bands. The 3-D CNN has a unique advantage in processing the 3-D data; therefore, it is used as the spatial-spectral feature extractor in this unified network. Finally, in order to optimize the parameters of both feature extractors simultaneously, the Bi-LSTM and 3-D CNN share a loss function to form a unified network. To evaluate the performance of the proposed framework, three datasets were tested for HSI classification. The results demonstrate that the performance of the proposed method is better than the current state-of-the-art HSI classification methods.

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with the PCA Network

Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a certain extent. In response to the above problems, this paper proposes a hyperspectral sea ice image classification method involving spectral-spatial-joint features based on the principal component analysis (PCA) network. First, the method uses the gray-level co-occurrence matrix (GLCM) and Gabor filter to extract textural and spatial information about sea ice. Then, the optimal band combination is extracted with a band selection algorithm based on a hybrid strategy, and the information hidden in the sea ice image is deeply extracted through a fusion of spectral and spatial features. Then, the PCA network is designed based on principal component analysis filters in order to extract the depth features of sea ice more effectively, and hash binarization maps and block histograms are used to enhance the separation and reduce the dimensions of features. Finally, the low-level features in the data form more abstract and invariant high-level features for sea ice classification. In order to verify the effectiveness of the proposed method, we conducted experiments on two different data collection points in Bohai Bay and Baffin Bay. The experimental results show that, compared with other single feature and spectral-spatial-joint feature algorithms, the proposed method achieves better sea ice classification results (94.15% and 96.86%) by using fewer training samples and a shorter training time.

Hyperspectral image classification with dual attention dense residual network

ABSTRACT Recent years, convolutional neural networks (CNNs) have attracted broad attention in hyperspectral image (HSI) classification. Most CNN-based HSI classification methods use image patches as the inputs of network. As the size increases, input patches may contain inhomogeneous pixels, which will cause damage to the classification results. In general, it needs to develop hierarchical architectures for CNNs to learn high-level feature representation. However, excessively deep architectures will cause overfitting and gradient vanishing, thus degrades the generalization performance of CNN. To address the above issues, a dual attention dense residual network (DADRN) is proposed for HSI classification. First, a dual attention module (DAM) is designed to reduce the damage of inhomogeneous pixels. It consists of a spectral attention unit and a spatial attention unit, and can achieve adaptive feature refinement. Second, a dense residual subnetwork (DRN) composed of dense convolutional blocks (DCBs) is proposed to extract more discriminative features from the output feature maps of the DAM. Specifically, the DCB achieves feature reuse mechanism through dense connection and channel concatenation operation, and the DRN adopts dense residual connections to alleviate overfitting and gradient vanishing. Experimental results on three benchmark HSI datasets demonstrate that the competitive advantage of proposed method over several state-of-the-art classification methods.

Multiscale Random Convolution Broad Learning System for Hyperspectral Image Classification

Deep learning is a powerful technique for image processing. Convolution neural network (CNN) is one of the widely used approaches for hyperspectral image (HSI) classification. These methods mostly need a time-consuming pretraining process to obtain deep features. Random patches networks (RPNets) provide a novel approach that the convolution kernel can be the original image without any pretraining process. In this letter, we propose a novel HSI classification method, multiscale random convolution broad learning system (MRC-BLS), which takes the spatial feature learning by an adaptive weighted mean filter as the convolution kernel to extract local spatial feature in the first layer. Different sizes of random convolution kernels can obtain a multiscale feature map. The weighted fusion of multiscale spatial features extracted by different sizes kernels can get better performance in HSI classification. A broad learning system (BLS) is an efficient classifier to classify images by the multiscale random feature. Experiments in three HSI data sets fully testify to the efficiency and satisfactory performance of the proposed method.

Adaptive Hash Attention and Lower Triangular Network for Hyperspectral Image Classification

Convolutional neural networks (CNNs), a kind of feedforward neural network with a deep structure, are one of the representative methods in hyperspectral image (HSI) classification. However, redundant information and interclass interference are common and challenging problems in HSI classification. In addition, if the spectral and spatial information is not properly extracted and analyzed, it will affect the classification performance of the network to a great extent. Aiming at these issues, this article proposes an HSI classification method based on an adaptive hash attention mechanism and a lower triangular network (AHA-LT). First, the attention mechanism is introduced in the preprocessing stage, which is composed of the spectral attention module and the adaptive hash spatial attention module in series. Then, the data processed by the attention mechanism are introduced into the lower triangular network (LTNet) to obtain the fused high-dimensional semantic features. Finally, we compress the features and obtain the output classification results through several fully connected layers. Among them, LTNet is composed of 2-D–3-D CNN and multiscale features. The network integrates the characteristics of multibranch, feature fusion, feature compression, and skip connections. Extensive experiments on four widely used HSI data sets show that the proposed method can obtain a great improvement in performance compared with the existing methods.

Hyperspectral Image Classification across Different Datasets: A Generalization to Unseen Categories

With the rapid developments of hyperspectral imaging, the cost of collecting hyperspectral data has been lower, while the demand for reliable and detailed hyperspectral annotations has been much more substantial. However, limited by the difficulties of labelling annotations, most existing hyperspectral image (HSI) classification methods are trained and evaluated on a single hyperspectral data cube. It brings two significant challenges. On the one hand, many algorithms have reached a nearly perfect classification accuracy, but their trained models are hard to generalize to other datasets. On the other hand, since different hyperspectral datasets are usually not collected in the same scene, different datasets will contain different classes. To address these issues, in this paper, we propose a new paradigm for HSI classification, which is training and evaluating separately across different hyperspectral datasets. It is of great help to labelling hyperspectral data. However, it has rarely been studied in the hyperspectral community. In this work, we utilize a three-phase scheme, including feature embedding, feature mapping, and label reasoning. More specifically, we select a pair of datasets acquired by the same hyperspectral sensor, and the classifier learns from one dataset and then evaluated it on the other. Inspired by the latest advances in zero-shot learning, we introduce label semantic representation to establish associations between seen categories in the training set and unseen categories in the testing set. Extensive experiments on two pairs of datasets with different comparative methods have shown the effectiveness and potential of zero-shot learning in HSI classification.

Distance-based hyperspectral open-set classification of deep neural networks

ABSTRACT Using deep learning, the supervised hyperspectral image (HSI) classification methods are based on the ideal assumption of closed sets, in which all testing classes are defined a priori. However, it is impossible to collect all classes while training in the open set setting where unknown classes can be submitted while testing. Traditional deep neural networks lack the ability to perceive the unknown classes without external supervision; therefore, they will misclassify them as known classes. This problem is called the open set classification (OSC). This paper proposes a distance-based OSC method for deep neural networks to solve the problem. The method introduces the class anchor clustering (CAC) classifier and loss to train deep neural networks, which can make known classes cluster around fixed class centres in the logit space. Boxplot and extreme value theory (EVT) are then used to determine the distance between input instances and their nearest class centres for unknown rejection without sacrificing the classification accuracy of known classes dramatically. Many experiments on HSI datasets indicated the validity of the HSI–OSC method.

Small sample hyperspectral image classification method based on memory association learning

Small sample hyperspectral image classification method based on memory association learning

Hyperspectral image classification via principal component analysis, 2D spatial convolution, and support vector machines

Hyperspectral image (HSI) classification has many applications in different diverse research fields. We propose a method for HSI classification using principal component analysis (PCA), 2D spatial convolution, and support vector machine (SVM). Our method takes advantage of correlation in both spatial and spectral domains in an HSI data cube at the same time. We use PCA to reduce the dimensionality of an HSI data cube. We then perform spatial convolution to the dimension-reduced data cube once and then to the convolved data cube for the second time. As a result, we have generated two convolved PCA output data cubes in a multiresolution way. We feed the two convolved data cubes to SVM to classify each pixel to one of the known classes. Experiments on three widely used hyperspectral data cubes (i.e., Indian Pines, Pavia University, and Salinas) demonstrate that our method can improve the classification accuracy significantly when compared to a few existing methods. Our method is relatively fast in terms of central processing unit computational time as well.

Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding

Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification.

Multiple Spectral Resolution 3D Convolutional Neural Network for Hyperspectral Image Classification

In recent years, benefiting from the rapid development of deep learning technology in the field of computer vision, the study of hyperspectral image (HSI) classification has also made great progress. However, compared with ordinary RGB images, HSIs are more like 3D cubes; therefore, it is necessary and beneficial to explore classification methods suitable for the very special data structure of HSIs. In this paper, we propose Multiple Spectral Resolution 3D Convolutional Neural Network (MSR-3DCNN) for HSI classification tasks. In MSR-3DCNN, we expand the idea of multi-scale feature fusion and dilated convolution from the spatial dimension to the spectral dimension, and combine 3D convolution and residual connection; therefore, it can better adapt to the 3D cubic form of hyperspectral data and make efficient use of spectral information in different bands. Experimental results on four benchmark datasets show the effectiveness of the proposed approach and its superiority as compared with some state-of-the-art (SOTA) HSI classification methods.

The use of KPCA over subspaces for cross-scale superpixel based hyperspectral image classification

ABSTRACT This paper introduces a new object-based spectral-spatial classification method for hyperspectral image. The kernel principal component analysis (KPCA) is firstly performed over subspaces (KPCAsub) derived from the original spectral domain, which incorporates linear information with nonlinear formulation. The obtained image is then processed via a feature-level fusion with superpixel segmentation at different scales. The final classification result is achieved by a cross-scale superpixel based (CSSP) decision fusion framework based on each individual operation of support vector machine. The resulting method, called KPCAsub-CSSP, contributes to better characterization under-limited sample condition, and promotes spectral-spatial integration in terms of echoing the complex distribution of ground objects. The experimental results on two real hyperspectral data sets demonstrate that the proposed method exhibits good performance in comparison to the other related methods.

Continuous Particle Swarm Optimization-Based Deep Learning Architecture Search for Hyperspectral Image Classification

Deep convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification. However, the most successful CNN architectures are handcrafted, which need professional knowledge and consume a very significant amount of time. To automatically design cell-based CNN architectures for HSI classification, we propose an efficient continuous evolutionary method, named CPSO-Net, which can dramatically accelerate optimal architecture generation by the optimization of weight-sharing parameters. First, a SuperNet with all candidate operations is maintained to share the parameters for all individuals and optimized by collecting the gradients of all individuals in the population. Second, a novel direct encoding strategy is devised to encode architectures into particles, which inherit the parameters from the SuperNet. Then, particle swarm optimization is used to search for the optimal deep architecture from the particle swarm. Furthermore, experiments with limited training samples based on four widely used biased and unbiased hyperspectral datasets showed that our proposed method achieves good performance comparable to the state-of-the-art HSI classification methods.

Lightweight Tensor Attention-Driven ConvLSTM Neural Network for Hyperspectral Image Classification

Recurrent neural networks, especially the convolutional long short-term memory (ConvLSTM), have attracted plenty of attention and shown promising results due to their ability in modeling long-term dependencies in many research fields. In this paper, a lightweight tensor attention-driven ConvLSTM neural network (TACLNN) is proposed for hyperspectral image (HSI) classification. Firstly, to reduce the trainable parameters and memory requirements of ConvLSTM (specifically, the 2-D version of LSTM, i.e., ConvLSTM2D), a lightweight ConvLSTM2D cell is developed by utilizing tensor-train decomposition, resulting in a TT-ConvLSTM2D cell, with which a spatial-spectral TT-ConvLSTM 2-D neural network (SSTTCL2DNN) is built. However, it is inevitable for SSTTCL2DNN to obtain lower accuracies for HSI classification. To recover the accuracy loss caused by the TT-ConvLSTM2D cell in SSTTCL2DNN, a learnable tensor attention residual block (TARB) module is built to further enhance its geometrical structure. When applied to three widely used HSI benchmarks, the proposed TACLNN model outperforms several state-of-the-art methods for HSI classification. In addition, the proposed TACLNN can effectively reduce the number of parameters and storage requirements achieving higher classification accuracies as compared to other competitive baselines.

A Lightweight 1-D Convolution Augmented Transformer with Metric Learning for Hyperspectral Image Classification.

Hyperspectral image (HSI) classification is the subject of intense research in remote sensing. The tremendous success of deep learning in computer vision has recently sparked the interest in applying deep learning in hyperspectral image classification. However, most deep learning methods for hyperspectral image classification are based on convolutional neural networks (CNN). Those methods require heavy GPU memory resources and run time. Recently, another deep learning model, the transformer, has been applied for image recognition, and the study result demonstrates the great potential of the transformer network for computer vision tasks. In this paper, we propose a model for hyperspectral image classification based on the transformer, which is widely used in natural language processing. Besides, we believe we are the first to combine the metric learning and the transformer model in hyperspectral image classification. Moreover, to improve the model classification performance when the available training samples are limited, we use the 1-D convolution and Mish activation function. The experimental results on three widely used hyperspectral image data sets demonstrate the proposed model’s advantages in accuracy, GPU memory cost, and running time.

Joint multi-mode cooperative classification algorithm for hyperspectral images

Hyperspectral image (HSI) classification is a challenging problem due to the high dimensional features, high intra-class variance, and limited prior information, and the classification is the basis for HSI applications. Active learning (AL) and semisupervised learning (SSL) are two promising approaches in the HSI classification. In AL, the traditional entropy query-by-bagging (EQB) algorithm only pays attention on uncertainty and ignore the diversity among the samples. Therefore, we propose averaged normalized entropy query-by-bagging (anEQB) algorithm. Meanwhile, the collaborative active learning and semisupervised learning framework (CASSL) may invoke many wrong pseudolabels and deteriorate the classification performance. To make up for the deficiency of CASSL, we complement different AL algorithms to constitute a multiple filtering mode semisupervised learning framework (MFMSLF). To further study, we introduce syncretic secondary filtering mode into multiple verification semisupervised framework and thus constitute a multiple secondary filtering mode semisupervised verification framework (MSFMSVF). We evaluate the performance of anEQB, MFMSLF, and MSFMSVF on different hyperspectral data sets and compare them with other state-of-the-art HSI classification methods. Numerical experimental results reveal the superior classification performance of anEQB, MFMSLF, and MSFMSVF, respectively. Experimental results also demonstrate that exploring the information and diversity of the samples from different criterion can improve the classification performance of the collaborative framework.

Feedback Attention-Based Dense CNN for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) methods based on convolutional neural network (CNN) continue to progress in recent years. However, high complexity, information redundancy, and inefficient description still are the main barriers to the current HSIC networks. To address the mentioned problems, we present a spatial-spectral dense CNN framework with a feedback attention mechanism called FADCNN for HSIC in this article. The proposed architecture assembles the spectral-spatial feature in a compact connection style to extract sufficient information independently with two separate dense CNN networks. Specifically, the feedback attention modules are developed for the first time to enhance the attention map with the semantic knowledge from the high-level layer of the dense model, and we strengthen the spatial attention module by considering multiscale spatial information. To further improve the computation efficiency and the discrimination of the feature representation, the band attention module is designed to emphasize the weight of the bands that participated in the classification training. Besides, the spatial-spectral features are integrated and mined intensely for better refinement in the feature mining network. The extensive experimental results on real hyperspectral images (HSI) demonstrate that the proposed FADCNN architecture has significant advantages compared with other state-of-the-art methods.

Multiscale Weighted Adjacent Superpixel-Based Composite Kernel for Hyperspectral Image Classification

This paper presents a composite kernel method (MWASCK) based on multiscale weighted adjacent superpixels (ASs) to classify hyperspectral image (HSI). The MWASCK adequately exploits spatial-spectral features of weighted adjacent superpixels to guarantee that more accurate spectral features can be extracted. Firstly, we use a superpixel segmentation algorithm to divide HSI into multiple superpixels. Secondly, the similarities between each target superpixel and its ASs are calculated to construct the spatial features. Finally, a weighted AS-based composite kernel (WASCK) method for HSI classification is proposed. In order to avoid seeking for the optimal superpixel scale and fuse the multiscale spatial features, the MWASCK method uses multiscale weighted superpixel neighbor information. Experiments from two real HSIs indicate that superior performance of the WASCK and MWASCK methods compared with some popular classification methods.