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Deep learning has been demonstrated to be a powerful nonlinear modeling method with end-to-end optimization capabilities for hyperspectral Images (HSIs). However, in real classification cases, obtaining labeled samples is often time-consuming and labor-intensive, resulting in few-shot training samples. Based on this issue, a multipath and multiscale Siamese network based on spatial-spectral features for few-shot hyperspectral image classification (MMSN) is proposed. To conduct classification with few-shot training samples, a Siamese network framework with low dependence on sample information is adopted. In one subnetwork, a spatial attention module (DCAM), which combines dilated convolution and cosine similarity to comprehensively consider spatial-spectral weights, is designed first. Then, we propose a residual-dense hybrid module (RDHM), which merges three-path features, including grouped convolution-based local residual features, global residual features and global dense features. The RDHM can effectively propagate and utilize different layers of features and enhance the expression ability of these features. Finally, we construct a multikernel depth feature extraction module (MDFE) that performs multiscale convolutions with multikernel and hierarchical skip connections on the feature scales to improve the ability of the network to capture details. Extensive experimental evidence shows that the proposed MMSN method exhibits a superior performance on few-shot training samples, and its classification results are better than those of other state-of-the-art classification methods. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) has a good effect in the supervised classification of hyperspectral images. In view of the shortcomings of the existing parallel structure SVM, this article proposes a non-parallel SVM model. Based on the traditional parallel boundary structure vector machine, this model adds an additional empirical risk minimization term to the original optimization problem by adding the least square term of the sample and obtains two non-parallel hyperplanes, respectively, forming a new non-parallel SVM algorithm to minimize the additional empirical risk of non-parallel SVM (Additional Empirical Risk Minimization Non-parallel Support Vector Machine, AERM-NPSVM). On the basis of AERM-NPSVM, the bias constraint is added to it, and AERM-NPSVM (BC-AERM-NPSVM) is further obtained. The experimental results show that, compared with the traditional parallel SVM model and the classical non-parallel SVM model, Twin Support Vector Machine (TWSVM), the new model, has a better effect in hyperspectral image classification and better generalization performance. [ABSTRACT FROM AUTHOR]

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 feedback ECNet (FECNet). This study was tested on four commonly used hyperspectral datasets [i.e., Indian Pine (IP), Pavia University (UP), Kennedy Space Center (KSC), and Salinas Valley (SV)] and on a higher resolution and complexly distributed land cover dataset (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. [ABSTRACT FROM AUTHOR]

In recent years, convolutional neural networks (CNNs) have been widely used for hyperspectral image classification, which show good performance. Compared with using sufficient training samples for classification, the classification accuracy of hyperspectral images is easily affected by a small number of samples. Moreover, although CNNs can effectively classify hyperspectral images, due to the rich spatial and spectral information of hyperspectral images, the efficiency of feature extraction still needs to be further improved. In order to solve these problems, a spatial–spectral attention fusion network using four branch multiscale block (FBMB) to extract spectral features and 3D-Softpool to extract spatial features is proposed. The network consists of three main parts. These three parts are connected in turn to fully extract the features of hyperspectral images. In the first part, four different branches are used to fully extract spectral features. The convolution kernel size of each branch is different. Spectral attention block is adopted behind each branch. In the second part, the spectral features are reused through dense connection blocks, and then the spectral attention module is utilized to refine the extracted spectral features. In the third part, it mainly extracts spatial features. The DenseNet module and spatial attention block jointly extract spatial features. The spatial features are fused with the previously extracted spectral features. Experiments are carried out on four commonly used hyperspectral data sets. The experimental results show that the proposed method has better classification performance than some existing classification methods when using a small number of training samples. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have exhibited extraordinary achievements in hyperspectral image (HSI) classification due to their detailed representation of features. However, the improvement of classification accuracy often leads to an evident increase in the complexity of the model, which makes it challenging for the model with the state-of-the-art performance to be applied in the actual scene. Considering MobileNetV3 as a lightweight feature extractor, this article proposes a model suitable for HSI classification based on MobileNetV3. To decrease the problem of massive redundant calculations in the existing spatial attention module, this article proposes a more concise and efficient spatial attention module based on the visual feature maps experiment. Besides, multiclass focal-loss is applied to solve the problem that the difficulty of classification varies for each sample. The experimental results demonstrate that in the case of using very few training sets, the proposed model can tremendously reduce the number of calculations and parameters while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

In recent decades, spatial feature extraction has greatly improved the performance of hyperspectral image (HSI) classification. This paper presents an HSI classification method based on multiple spatial features extraction and fusion (MSFs-EF). The method consists of five sequential steps. 1- Principal component analysis is applied for HSI dimensionality reduction. 2- A mean curvature filter is used to extract the spatial texture features from the HSI. 3- The spatial correlation features are obtained using a domain transform normalized convolution filter. 4- Spatial texture features and spatial correlation features are combined. 5- The multiple spatial features are classified using the Large Margin Distribution Machine. Three hyperspectral data sets are used to verify the performance. This method improves the accuracy of HSI classification compared with SVM method, edge-preserving filter, recursive filter method, and deep learning method. In the case of ratios of training samples of 5%, 0.6%, and 5%, the overall accuracy of three data sets reaches 98.23%, 99.17%, and 98.21% respectively, and are about 1.3%∼19%, 0.2%∼13%, and 0.4%∼13% higher than other fourteen algorithms. In the case of ratios of training samples of 10%, 1%, and 10%, the overall accuracy of the three data sets reaches 98.63%, 99.53%, and 98.99%, respectively and still outperform other methods. [ABSTRACT FROM AUTHOR]

Hyperspectral image feature extraction generally does not consider the optimisation of structure element types, resulting in the loss of spatial correlation information. To solve this issue, this paper proposes a novel adaptive classification method (MPDTNC-SVM), in which spatial information is extracted by Morphological Profile Filter (MPF) and Domain Transform Normalised Convolution Filter (DTNCF). First, MPF extracts the spatial features on multiple principal component analysis (PCA) components of the hyperspectral image, and DTNCF works over all spectral bands to extract spatially correlated features. The two spatial features are then combined and fed into Support Vector Machine (SVM). Second, a two-step optimisation is implemented in the classification process. Specifically, the best structure of MPF is chosen for classification. Next, the optimal parameters of the structural elements are obtained through iterative classification optimisation, with the best classification performance produced in the process. Experimental results of actual hyperspectral images show that the proposed MPF and DTNCF with SVM (MPDTNC-SVM) method is superior to other classification methods, including Edge-Preserving Filter and Recursive Filter method, morphological feature-based methods, and the SVM methods with raw spectral features, reduced-dimensional and spatial-spectral information. [ABSTRACT FROM AUTHOR]

In recent years, the spatial texture features obtained by filtering have become a hot research topic to improve hyperspectral image classification, but spatial correlation information is often lost in spatial texture information extraction. To solve this problem, a spectral-spatial classification method based on guided filtering and by the algorithm Large Margin Distribution Machine (LDM) is proposed. More specifically, the spatial texture features can be extracted by a Guided filter (GDF) from hyperspectral images whose dimensionality is reduced by a Principal Component Analysis (PCA). Spatial correlation features of the hyperspectral image are then obtained using a Domain Transform Interpolated Convolution Filter. The last step is to fuse spatial texture features and correlation features for classification by LDM. The experimental results using the actual hyperspectral image indicate that the proposed GDFDTICF-LDM method is superior to other classification methods, such as the original Support Vector Machine (SVM) with raw spectral features, dimensionality reduction features and spatial-spectral information, methods of edge-preserving filter and recursive filter, and LDM-based methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spatial-dictionary (SD) for collaborative representation classification (SCRC) of hyperspectral images. The proposed method consists of four main steps. First, we extract spatial features using 2-D Gabor filters and stack them with spectral features. Second, the SD is constructed by incorporating the spatial information of sparse vectors into the dictionary optimization process. Third, a multiple-mapping kernel is exploited to further integrate spatial information into the CRC framework. Lastly, the test samples are allocated with the class labels. Experimental results obtained on two hyperspectral datasets demonstrate that the proposed SCRC method can yield higher classification accuracy with much lower computational cost when compared to other traditional classifiers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel residual fusion classification method for hyperspectral image using spatial–spectral information, abbreviated as RFC-SS. The RFC-SS method first uses the Gabor texture features and the non-parametric weighted spectral features to describe the hyperspectral image from both aspects of spatial and spectral information. Then it applies the residual fusion method to save the useful information from different classification methods, which can greatly improve the classification performance. Finally, the test sample is assigned to the class that has the minimal fused residuals. The RFC-SS classification method is tested on two classical hyperspectral images (i.e. Indian Pines, Pavia University). The theoretical analysis and experimental results demonstrate that the RFC-SS classification method can achieve a better performance in terms of overall accuracy, average accuracy, and the Kappa coefficient when compared to the other classification methods. [ABSTRACT FROM PUBLISHER]

In recent years, due to its powerful feature extraction ability, the deep learning method has been widely used in hyperspectral image classification tasks. However, the features extracted by classical deep learning methods have limited discrimination ability, resulting in unsatisfactory classification performance. In addition, due to the limited data samples of hyperspectral images (HSIs), how to achieve high classification performance under limited samples is also a research hotspot. In order to solve the above problems, this paper proposes a deep learning network framework named the three-dimensional coordination attention mechanism network (3DCAMNet). In this paper, a three-dimensional coordination attention mechanism (3DCAM) is designed. This attention mechanism can not only obtain the long-distance dependence of the spatial position of HSIs in the vertical and horizontal directions, but also obtain the difference of importance between different spectral bands. In order to extract the spectral and spatial information of HSIs more fully, a convolution module based on convolutional neural network (CNN) is adopted in this paper. In addition, the linear module is introduced after the convolution module, which can extract more fine advanced features. In order to verify the effectiveness of 3DCAMNet, a series of experiments were carried out on five datasets, namely, Indian Pines (IP), Pavia University (UP), Kennedy Space Center (KSC), Salinas Valley (SV), and University of Houston (HT). The OAs obtained by the proposed method on the five datasets were 95.81%, 97.01%, 99.01%, 97.48%, and 97.69% respectively, 3.71%, 9.56%, 0.67%, 2.89% and 0.11% higher than the most advanced A2S2K-ResNet. Experimental results show that, compared with some state-of-the-art methods, 3DCAMNet not only has higher classification performance, but also has stronger robustness. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images. [ABSTRACT FROM AUTHOR]

For remote sensing scene image classification, many convolution neural networks improve the classification accuracy at the cost of the time and space complexity of the models. This leads to a slow running speed for the model and cannot realize a trade-off between the model accuracy and the model running speed. As the network deepens, it is difficult to extract the key features with a sample double branched structure, and it also leads to the loss of shallow features, which is unfavorable to the classification of remote sensing scene images. To solve this problem, we propose a dual branch multi-level feature dense fusion-based lightweight convolutional neural network (BMDF-LCNN). The network structure can fully extract the information of the current layer through 3 × 3 depthwise separable convolution and 1 × 1 standard convolution, identity branches, and fuse with the features extracted from the previous layer 1 × 1 standard convolution, thus avoiding the loss of shallow information due to network deepening. In addition, we propose a downsampling structure that is more suitable for extracting the shallow features of the network by using the pooled branch to downsample and the convolution branch to compensate for the pooled features. Experiments were carried out on four open and challenging remote sensing image scene data sets. The experimental results show that the proposed method has higher classification accuracy and lower model complexity than some state-of-the-art classification methods and realizes the trade-off between model accuracy and model running speed. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have exhibited excellent performance in hyperspectral image classification. However, due to the lack of labeled hyperspectral data, it is difficult to achieve high classification accuracy of hyperspectral images with fewer training samples. In addition, although some deep learning techniques have been used in hyperspectral image classification, due to the abundant information of hyperspectral images, the problem of insufficient spatial spectral feature extraction still exists. To address the aforementioned issues, a spectral–spatial attention fusion with a deformable convolution residual network (SSAF-DCR) is proposed for hyperspectral image classification. The proposed network is composed of three parts, and each part is connected sequentially to extract features. In the first part, a dense spectral block is utilized to reuse spectral features as much as possible, and a spectral attention block that can refine and optimize the spectral features follows. In the second part, spatial features are extracted and selected by a dense spatial block and attention block, respectively. Then, the results of the first two parts are fused and sent to the third part, and deep spatial features are extracted by the DCR block. The above three parts realize the effective extraction of spectral–spatial features, and the experimental results for four commonly used hyperspectral datasets demonstrate that the proposed SSAF-DCR method is superior to some state-of-the-art methods with very few training samples. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) exhibit excellent performance in hyperspectral image classification (HSIC) and have attracted significant interest. Nevertheless, the common CNN-based classification techniques still suffer from the following drawbacks. 1) Although deep CNNs can effectively extract features from hyperspectral images, a network that is too deep often leads to negative effects such as overfitting, vanishing gradients, and decreased accuracy. 2) Most of these models for HSIC do not fully consider the strong complementarity and correlation between features at different levels. To address these issues, firstly, a self-regularization parameter correction non-monotonic activation function, called Lush, was first proposed. Then, this study proposes a novel bias update method that can update network bias parameters more efficiently. On the basis of the above, a Lush multi-layer feature fusion polarization network (LMFFBNet) for hyperspectral image classification was proposed in this study. It designs a new after-melting technology to fuse deep and shallow features in multiple stages and layers, which can obtain more discriminative features, thereby improving the classification performance of hyperspectral images. Extensive experiments on five challenging datasets (Indian Pines, Pavia University, Kennedy Space Center, Salinas Valley, and Houston) have demonstrated that compared to some state-of-the-art methods, the proposed LMFFBNet method can provide better classification performance for hyperspectral images and has strong robustness. This fully proves the effectiveness of the LMFFBNet method. [ABSTRACT FROM AUTHOR]

In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method based on the fusion of two spatial information, which will be classified by a large margin distribution machine (LDM). First, the spatial texture information is extracted from the top of the principal component analysis for hyperspectral images by a curvature filter (CF). Second, the spatial correlation information of a hyperspectral image is completed by using domain transform recursive filter (DTRF). Last, the spatial texture information and correlation information are fused to be classified with LDM. The experimental results of hyperspectral images classification demonstrate that the proposed curvature filter and domain transform recursive filter with LDM(CFDTRF-LDM) method is superior to other classification methods. [ABSTRACT FROM AUTHOR]