Features Of Hyperspectral Image Research Articles

Global to Local: A Hierarchical Detection Algorithm for Hyperspectral Image Target Detection

Hyperspectral image (HSI) has received considerable attention in the field of target detection due to its powerful ability to capture the spectral information of land covers, and plenty of detection algorithms have been explored. However, these methods generally leverage the difference between the spectrum of the target to be detected and the background spectrum to accomplish target detection, and so are susceptible to the problem of spectral variability. In this article, we propose a global-to-local hierarchical detection algorithm for HSI (G2LHTD). Firstly, extended morphological attribute profile (EMAP) is first used to model global spatial texture information from HSI. Subsequently, a diverse-direction constrained energy minimization (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CEM) detector is developed to consider the spatial information within eight neighborhoods around each pixel in HSI, yielding comprehensive local spatial information. More substantially, to effectively discriminate the neighborhood information in diverse directions, we devise an adaptive neighborhood feature aggregation (ANFA) strategy, which will comprehensively evaluate the significance of neighborhood information in diverse directions. As a result, the spatial features of HSI can be comprehensively considered for hyperspectral target detection (HTD). Extensive experiments, conducted on four standard datasets, demonstrate the effectiveness of the proposed method. The codes of this work will be available at https://github.com/zhonghaocheng/G2LHTD_Master for the sake of reproducibility.

Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network for Hyperspectral Image Classification.

Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), such as Graph Attention Networks (GAT), are two classic neural network models, which are applied to the processing of grid data and graph data respectively. They have achieved outstanding performance in hyperspectral images (HSIs) classification field, which have attracted great interest. However, CNN has been facing the problem of small samples and GNN has to pay a huge computational cost, which restrict the performance of the two models. In this paper, we propose Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network (WFCG) for HSI classification, by using the characteristics of superpixel-based GAT and pixel-based CNN, which proved to be complementary. We first establish GAT with the help of superpixel-based encoder and decoder modules. Then we combined the attention mechanism to construct CNN. Finally, the features are weighted fusion with the characteristics of two neural network models. Rigorous experiments on three real-world HSI data sets show WFCG can fully explore the high-dimensional feature of HSI, and obtain competitive results compared to other state-of-the art methods.

A Hybrid CNN Based on Global Reasoning for Hyperspectral Image Classification

In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral images (HSIs) classification. However, 2-D CNN, 3-D CNN, and even the newly emerged hybrid CNN (HCNN) all require multiple or deep CNN layers to obtain excellent classification performance, which inevitably results in the high complexity and the need for a large number of training samples. Moreover, as a local operator, convolution is challenging to fully use global information. To solve the above two issues, we design a HCNN based on global reasoning (GloRe-HCNN) for HSI classification. On the one hand, the GloRe-HCNN uses only one layer of 3-D CNN and one layer of 2-D CNN to jointly extract the spatial&#x2013;spectral features of HSI. On the other hand, we contrive a spatial&#x2013;spectral global reasoning unit (SS-GloRe-Unit) to take the place of stacked multilayer 3-D CNN for extracting global features fully. We select small training samples in three standard datasets and compare them with state-of-the-art CNN methods. Numerous experiments show that our GloRe-HCNN performs advanced performance.

A Novel Band Selection and Spatial Noise Reduction Method for Hyperspectral Image Classification

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral-spatial features of HSI. We proposed a new Neighboring band Grouping and Normalized Matching Filter (NGNMF) for BS, which can reduce the data dimension whilst preserve the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intra-class variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral-spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods.

Feature Fusion via Deep Residual Graph Convolutional Network for Hyperspectral Image Classification

Recently, graph convolutional network (GCN) has been applied for hyperspectral image (HSI) classification and obtained better performance. The main issue in HSI classification is that the high-resolution HSI contains more complex spectral-spatial structure information. However, the previous GCN-based methods applied in HSI classification only adopted a shallow GCN layer and they can not extract the deeper discriminative features. In addition, these methods ignored the complementary and correlated information among multi-order neighboring information extracted by multiple GCN layers. In this letter, a novel feature fusion via deep residual graph convolutional network is proposed to explore the internal relationship among HSI data. On the one hand, benefiting from residual learning to alleviate the over-smoothing problem, we can construct deep GCN layers to excavate deeper abstract features of HSI. On the other hand, we fuse the outputs of different GCN layers, and thus, the local structural information within multi-order neighborhood nodes can be fully utilized. Extensive experiments on four real HSI data sets, including Indian Pines, Pavia University, Salinas, and Houston University, demonstrate the superiority of the proposed method compared with other state-of-the-art methods in various evaluation criteria.

Dual-Concentrated Network With Morphological Features for Tree Species Classification Using Hyperspectral Image

At present, deep learning is a hot topic in the field of the classification of hyperspectral image (HSI), and it has aroused wide attention. However, in fine-grained classification tasks, such as tree species classification, the uncertain spectrum remains the major factor restraining the classification performance. In order to solve the dilemma of forest tree species classification, a Dual-concentrated Network with Morphological Features (DNMF) is proposed. Firstly, mathematical morphology is used to extract the morphological features of HSI. Then, coarse-grained information is extracted from the original hyperspectral data, and fine-grained information is extracted from morphological features. After that, both morphological representations and spectral inputs are fed into DNMF, and the overall evaluation index and visual image are obtained. The advantage of DNMF is that it decouples the spatial and spectral information, and a multi-source information fusion process is then simulated. Accordingly, DNMF obtains high tree species classification accuracy. In order to verify the superiority of DNMF, we choose Gaofeng State-owned Forest Farm (GSFF) in Guangxi Province and the Belgium dataset which was collected near the western part of Belgium as the research area. Related experiments demonstrate that the DNMF model achieves clearly better classification performance over other competitive baselines.

Enhanced Spectral–Spatial Residual Attention Network for Hyperspectral Image Classification

Deep learning has achieved good performance in hyperspectral image classification (HSIC). Many methods based on deep learning use deep and complex network structures to extract rich spectral and spatial features of hyperspectral images (HSIs) with high accuracy. During the process, how to accurately extract the features and information from pixel blocks in HSIs is important. All of the spectral features are treated equally in classification, and the input of the network often contains much useless pixel information, leading to a low classification result. To solve this problem, an enhanced spectral-spatial residual attention network (ESSRAN) is proposed for HSIC in this paper. In the proposed network, the spectral-spatial attention network (SSAN), residual network (ResNet) and long-short term memory (LSTM) are combined to extract more discriminative spectral and spatial features. More specifically, SSAN is first applied to extract image features by using the spectral attention module to emphasize useful bands and suppress useless bands. The spatial attention module is used to emphasize pixels that have same category with the central pixel. Then, these obtained features are fed into an improved ResNet, which adopts LSTM to learn representative high-level semantic features of the spectral sequences, since the use of ResNet can prevent gradient disappearance and explosion. The proposed ESSRAN model is implemented on three commonly used HSI datasets and compared to some state-of-the-art methods. The results confirm that ESSRAN effectively improves accuracy.

Hyperspectral Image Classification Based on Expansion Convolution Network

In recent years, convolutional neural networks (CNNs) have achieved excellent performance in hyperspectral image classification and have been widely used. However, the convolution kernel used in traditional CNN has the limitation of single scale which is not conducive to the improvement of hyperspectral classification performance. In addition, training a classification network of high-dimensional data based on limited labeled samples is still one of the challenges of hyperspectral image classification. To solve the above problems, a hyperspectral image classification method based on expansion convolution network (ECNet) is proposed. The expansion convolution injects holes into the standard convolution kernel to expand the receptive field (RF), so as to extract more context features. Because the shallow features of hyperspectral images contain more location and detail information, while the deep features contain stronger semantic information, in order to further enhance the correlation between deep and shallow information, inspired by ResNet, a similar feedback block (SFB) is introduced on the basis of ECNet, and the deep features and shallow features are fused through this feedback mechanism. Thus, an improved version of ECNet method is obtained, which is called FECNet. This study was tested on four commonly used hyperspectral data sets (i.e. Indian Pine (IP), Pavia University (UP), Kennedy Space Center (KSC), Salinas Valley (SV)) and on a higher resolution and complexly distributed land cover data set (University of Houston (HT)). The experimental results show that the proposed method has better classification performance than some state-of-the art methods, which shows that FECNet has a certain potential in hyperspectral image classification.

Accelerated Adaptive Feature Balance Technique Based on TEMD for Hyperspectral Classification

Tridimensional empirical mode decomposition (TEMD) has been successfully employed as a powerful tool for hyperspectral classification. It achieves good performance to analyze nonlinear/non-stationary data and capture abstract features of hyperspectral images (HSIs). However, the major challenge is how to adequately exploit and balance decomposed edge-wise (EW) and trend-wise (TW) features. To address this issue, we propose an accelerated adaptive feature balance technique (A²FBT) based on TEMD. Due to complex discriminant information, A²FBT initially decomposes the HSI into varying oscillations adequately, including several tridimensional intrinsic mode functions (TIMFs) and one residual (RES). They contain high frequency EW and low frequency TW features, respectively. To further accelerate the decomposition, a k-d tree based search strategy is proposed for filter size selection. To bridge the gaps between the RES and all TIMFs, a novel adaptive feature balance strategy is presented by assigning adaptive trade-offs. We aim to balance multiple features adequately according to the intrinsic discriminant information. A combined metric based strategy is provided for trade-off calculation, measuring the mutual similarities between oscillations comprehensively. A²FBT pays more attention to these similarities, reinforcing the respective contributions of EW and TW features. Experiments on four typical hyperspectral datasets illustrate the effectiveness of A²FBT compared with several state-of-the-art techniques.

Multiscale Densely Connected Attention Network for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) based on deep learning has always been a research hot spot in the field of remote sensing. However, most of the classification models extract relevant features based on fixed-scales convolution kernels, which ignores the complex features of hyperspectral images (HSIs) at different scales and impairs the classification accuracy. To solve this problem, a multiscale densely connected attention network (MSDAN) is proposed for HSIC. First, the model adopts three different scales modules with dense connection to enhance classification performance, strengthen feature reuse, prevent overfitting and gradient disappearance. Besides, in order to reduce the model parameters and strengthen the extraction of spatial–spectral features, the traditional three-dimensional convolution is replaced by three-dimensional spectral convolution block and three-dimensional spatial convolution block. Furthermore, the spectral–spatial–channel attention is embedded into the end of each scale to enhance the favorable features for classification and further extract the discriminant features of the corresponding scale. Finally, the key feature extraction module is developed to extract multiscale fusion features to further enhance the classification performance of the network. The experimental results carried out on real HSIs show that the proposed MSDAN architecture has significant advantages compared with other most advanced methods.

SSTNet: Spatial, Spectral, and Texture Aware Attention Network Using Hyperspectral Image for Corn Variety Identification

Currently, most existing methods using hyperspectral image to assist seed identification only consider the spectral information but ignore the spatial information resulting in unsatisfactory classification results. To cope with this issue, we propose a spatial, spectral, and texture-aware attention network to identify corn varieties, called SSTNet. Specifically, we first employ 3D convolution to extract the spatial and inter-spectral features. Subsequently, we utilize 2D convolution to extract the spatial and texture features. Meanwhile, we embed an attention mechanism into the 2D convolution module to further refine the spatial and texture features. The advantageous complementary properties of 3D and 2D convolutions allow the spatial and textural features of hyperspectral images to be fully exploited. Besides, we construct a hyperspectral image dataset including 1200 samples of 10 corn varieties. Experiments on our proposed dataset demonstrate that our SSTNet outperforms the state-of-the-art methods for identifying corn varieties.

HyperspectraI Image Classification With Diverse Region Multiscale Feature Extraction and Spectral Imaging

We propose a dual-branch multi-region gaussian pyramid based multi-scale feature extraction and spectral feature extraction network. We use the PCA algorithm to reduce the dimensionality of the data before feature extraction, but the degree of data downscaling is different for the two-branch network. For the spatial information of hyperspectral images, we use a multi-region piecewise gaussian pyramid downsampling method to generate multi-scale and multi-resolution image data and use an improved resnet network to extract spatial information, so that the network can extract specific contextual features of hyperspectral images. For the spectral information of hyperspectral images, we use the method of imaging spectral information, first reducing the dimension of the spectral data and then expanding the spectral data into an image of <i>N</i>&#x00D7;<i>N</i>. By expanding into an image, the resnet network is introduced to extract spectral information, but the number of network layers is different from that of the resnet network for obtaining spatial information, which can solve the problem of low correct classification rate due to changes in similar spectral data. Finally, the spatial and spectral features after the dual-branch feature extraction network are combined into a fully connected network for classification, and the fusion of the two features can improve the classification accuracy. Our experiments on three commonly used datasets show that the method can improve the accuracy of the classifier.

Multiscale Alternately Updated Clique Network for Hyperspectral Image Classification

Recently, deep learning has drawn significant attention in hyperspectral image (HSI) classification. With the growth of network depth and feature integration, deep learning demands abundant labeled samples to optimize many parameters. Unfortunately, most hyperspectral data are unlabeled and the available labeled samples are extremely limited. How to obtain richer features under limited training samples is a challenge for HSI classification. To tackle this issue, a new supervised multiscale alternately updated clique network (MSCN) is proposed for HSI classification to fully employ HSI features in different scales. Based on the Clique Block, we design the multiscale alternately updated clique block (MSCB) that applies convolution kernels of various sizes to adaptively exploit the multiscale HSI information and merge them within the block. Meanwhile, the recurrent feedback architecture is introduced to reuse high-level visual information and network parameters. The proposed MSCN includes two MSCBs to capture the multiscale spectral and spatial information in turn. The MSCN improves the information flow and the efficiency of parameter tuning through the feedback mechanism and the cross-utilization of multiscale feature. It not only obtains more abstract HSI information, but also reduces the network depth and the number of parameters, thereby improving the classification accuracy under limited samples. To certify the validity of the proposed MSCN, experiments are conducted on three real HSI datasets and compared with multiple state-of-the-art deep learning-based approaches. The experimental results demonstrate that the presented multiscale network achieves superior performance, especially in the case of a small number of training samples.

Multibranch Feature Fusion Network With Self- and Cross-Guided Attention for Hyperspectral and LiDAR Classification

The effective fusion of multi-source data helps to improve performance of land cover classification. Most existing convolutional neural network (CNN) based methods adopt an early/late fusion strategy to fuse the low-level/high-level features for classification, which still has two inherent challenges: i) the conventional convolution operation performs a weighted average operation on each pixel in the receptive field, which will reduce the discriminability of the center pixel due to the influence of the interference pixels, and ii) the spatial-spectral features of the hyperspectral image (HSI), the elevation features of light detection and ranging (LiDAR), and the complementary features between the multimodal data are not fully exploited, which results in the reduction of classification accuracy. In this paper, an effective multi-branch feature fusion network with self- and cross-guided attention (MB2FscgaNet) is proposed for joint classification of LiDAR and HSI. The main concern of this paper is how to accurately estimate more effective spectral-spatial-elevation features and yield more effective transfer in network. Specifically, MB2FscgaNet adopts a multi-branch feature fusion architecture to fully exploit the hierarchical features from LiDAR and HSI level by level. At each level of the network, a self- and cross-guided attention (SCGA) is developed to assign higher weight to interesting areas and channels of LiDAR and HSI feature maps to obtain refined spectral-spatial-elevation features and provide complementary information cross guidance between LiDAR and HS. We further designed a spectral supplement module (SeSuM) to improve the discriminative ability of the center pixel. Comparative classification results and ablation studies demonstrate that the proposed MB2FscgaNet achieves competitive performance against state-of-the-art methods.

Curvature Filters-Based Multiscale Feature Extraction for Hyperspectral Image Classification

Exploring fast and effective spectral-spatial feature extraction algorithms for hyperspectral image (HSI) classification is one of the most focus problems in current hyperspectral remote-sensing research. Generally, the size of homogeneous regions in HSIs is not consistent in real scenario and real scenario usually consist of ground objects of different scales. Multiscale strategy starts to be used to construct discriminative features at different scales for HSI classification in recent years. To efficiently characterize the multiscale spectral-spatial features of HSIs, a curvature filters-based multiscale feature extraction method with multiscale superpixel segmentation constraint is proposed. The proposed algorithm is composed of the following major stages. First, global multiscale spectral-spatial features are efficiently extracted via progressively curvature filtering and downsampling operations, which can be regarded as an image pyramid decomposition method. Next, a multiscale superpixel segmentation strategy is applied on the first layer of the image pyramid, and a weighted mean operation is applied within and among superpixels to extract the local multiscale spatial features (LMSFs). Finally, the global multiscale curvature features (GMCFs) and the superpixel segmentation-based LMSFs are fused to form the final multiscale spectral-spatial features for classification purposes. To verify the capabilities of the proposed method, comprehensive experiments are performed on five real hyperspectral datasets. Experimental results demonstrate that the proposed method can significantly improve the classification accuracies compared to several standard HSI feature extraction and classification methods, especially when the number of samples for training is limited.

Probing the Metabolic Landscape of Plant Vascular Bundles by Infrared Fingerprint Analysis, Imaging and Mass Spectrometry.

Fingerprint analysis is a common technique in forensic and criminal investigations. Similar techniques exist in the field of infrared spectroscopy to identify biomolecules according to their characteristic spectral fingerprint features. These unique markers are located in a wavenumber range from 1800 to 600 cm−1 in the mid infrared region. Here, a novel bioanalytical concept of correlating these spectral features with corresponding mass spectrometry datasets to unravel metabolic clusters within complex plant tissues was applied. As proof of concept, vascular bundles of oilseed rape (Brassica napus) were investigated, one of the most important and widely cultivated temperate zone oilseed crops. The link between mass spectrometry data and spectral data identified features that co-aligned within both datasets. Regions of origin were then detected by searching for these features in hyperspectral images of plant tissues. This approach, based on co-alignment and co-localization, finally enabled the detection of eight distinct metabolic clusters, reflecting functional and structural arrangements within the vascular bundle. The proposed analytical concept may assist future synergistic research approaches and may lead to biotechnological innovations with regard to crop yield and sustainability.

Hyperspectral Image Classification Based on SE-Res2Net and Multi-Scale Spatial Spectral Fusion Attention Mechanism

<p indent=0mm>In order to extract more discriminative features for hyperspectral image and prevent the network from degrading caused by deepening, a novel multi-scale feature extraction module SE-Res2Net based on the new dimensional residual network (Res2Net) and squeeze and exception network (SENet), and a multi-scale spectral-spatial fusion attention module is developed for hyperspectral image classification. In order to overcome the degradation problem caused by network deepening, the SE-Res2Net module uses channel grouping to extract fine-grained multi-scale features of hyperspectral images, and gets multiple receptive fields of different granularity. Then, the channel optimization module is employed to quantify the importance of the feature maps at the channel level. In order to optimize the features from spatial and spectral dimensions simultaneously, a multi-scale spectral-spatial fusion attention module is designed to mine the relationship between different spatial positions and different spectral dimensions at different scales using asymmetric convolution, which can not only reduce the computation, but also effectively extract the discriminative spectral-spatial fusion features, and further improve the accuracy of hyperspectral image classification. Comparison experiments on three public datasets, Indian Pines, University of Pavia and Grss_dfc_2013 show that the proposed method has higher overall accuracy (OA), average accuracy (AA) and Kappa coefficient compared to other state-of-the-art deep networks.

Deep global-local transformer network combined with extended morphological profiles for hyperspectral image classification

Recently, deep learning models based on convolutional neural networks (CNN) remain dominant in hyperspectral image (HSI) classification. However, there are some problems in CNN models, such as not good at modeling the long-distance dependencies and obtaining global context information. Different from the existing CNN-based models, an innovative classification method based on the transformer model is proposed to further improve the classification accuracy of HSI. Specifically, the proposed method first extracts the extended morphological profile (EMP) features of HSI to make full use of the spatial and spectral information while effectively reducing the number of bands. Next, a deep network model is constructed by introducing the transformer-iN-transformer (TNT) modules to carry out end-to-end classification. The outer and inner transformer models in the TNT module can extract the patch-level and pixel-level features, respectively, to make full use of the global and local information in the input EMP cubes. Experimental results on three public HSI data sets show that the proposed method can achieve better classification performance than the existing CNN-based models. In addition, using the transformer-based deep model without convolution to classify HSI provides a new idea for related research.

Contrastive Learning Based on Transformer for Hyperspectral Image Classification

Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance. However, the availability of labeled data is limited due to the significant human resources and time costs of labeling hyperspectral data. Unsupervised learning for hyperspectral image classification has thus received increasing attention. In this paper, we propose a novel unsupervised framework based on a contrastive learning method and a transformer model for hyperspectral image classification. The experimental results prove that our model can efficiently extract hyperspectral image features in unsupervised situations.

Hyperspectral image classification with deep 3D capsule network and Markov random field

To address the existing problems of capsule networks in deep feature extraction and spatial-spectral feature fusion of hyperspectral images, this paper proposes a hyperspectral image classification method that combines a deep residual 3D capsule network and Markov random field. Based on this method, the deep spatial-spectral features of hyperspectral images are extracted using the deep residual 3D convolutional structure, the vector capsules of the features are obtained by the initial capsule layer and mapped into probability capsules via the 3D dynamic routing mechanism to construct the classification probability map, and the spatial structure of the classification results is regularised by the Markov random field to further improve the classification accuracy and performance of the images. Two sets of benchmark hyperspectral images, namely Indian Pines and Pavia University data sets, were used to conduct comparative experiments and ablation study. The experimental results showed that, compared with the conventional convolutional neural network and existing capsule network models, the proposed method not only improves the classification accuracy of the images but also partly eliminates the category noise and affords a more regular classification probability map.