Feature Extraction Of Hyperspectral Images Research Articles

An Effective Randomized Algorithm for Hyperspectral Image Feature Extraction

Analyzing the spectral and spatial characteristics of Hyperspectral Imaging (HSI) in a three-dimensional space is a challenging task. Recently, there have been developments in 3D feature extraction methods based on tensor decomposition, which allow for the effective utilization of both global and local information in HSI. These methods also explore the inherent low-rank properties of HSI through tensor decomposition. In this paper, we propose a new approach called variable randomized T-product decomposition (Vrt-SVD), which is a variation of Tensor Singular Spectral Analysis. The goal of this approach is to improve the efficiency of tensor methods for feature extraction and reduce artifacts of image processing. By using a randomized algorithm based on the variable t-SVD, we are able to capture both global and local spatial and spectral information in HSI efficiently, which enables us to explore its low-rank characteristics. To evaluate the effectiveness of the extracted features, we use a Support Vector Machine (SVM) classifier to assess the accuracy of image classification. By conducting numerous numerical experiments, we provide strong evidence to show that the proposed method outperforms several advanced feature extraction techniques.

Semi-supervised spatial spectral local discriminant analysis for hyperspectral image feature extraction

Semi-supervised spatial spectral local discriminant analysis for hyperspectral image feature extraction

LG-3D-SSA: Local and Global Three Dimensional SSA to Achieve Efficient Spectral-Spatial Feature Extraction of Hyperspectral Images

LG-3D-SSA: Local and Global Three Dimensional SSA to Achieve Efficient Spectral-Spatial Feature Extraction of Hyperspectral Images

An efficient dual feature fusion model of feature extraction for hyperspectral images

ABSTRACT Conventional feature extraction (FE) and spatial or context-preserving filters have been extensively studied when applying hyperspectral images (HSIs). However, there still exists some issues to resolve, such as the destruction of the complete structural information because of unfolded 2-D matrix before extracting, the unchanged or even increased number of resulting features from the additional context or spatial-preserving filters, and overreliance on experience to choose retained dimensionality that significantly improves the operating time. This article presents an efficient FE framework, i.e., a dual feature fusion model (DFFM), to address these issues. Specifically, a novel two-order feature fusion (FF) weighted on partial Shannon’s entropy is proposed to hold low dimensional characteristics. Then, a valid three-order FF using constrained Tucker compression is performed on the resulting elements, containing the intact spatial structure and saving computing costs. It can also automatically select a suitable number of features to keep and is robust to noise and training sets. Comparative experiments on three benchmark HSIs were performed to verify the efficiency of DFFM in cases of different training sizes. All the experimental results show that this framework is robust and effective, outperforming several state-of-the-art techniques in classification precision and execution time.

An adaptive graph embedding method for feature extraction of hyperspectral images based on approximate NMR model

An adaptive graph embedding method for feature extraction of hyperspectral images based on approximate NMR model

Local and Global Spectral Features for Hyperspectral Image Classification

Hyperspectral images (HSI) contain powerful spectral characterization capabilities and are widely used especially for classification applications. However, the rich spectrum contained in HSI also increases the difficulty of extracting useful information, which makes the feature extraction method significant as it enables effective expression and utilization of the spectrum. Traditional HSI feature extraction methods design spectral features manually, which is likely to be limited by the complex spectral information within HSI. Recently, data-driven methods, especially the use of convolutional neural networks (CNNs), have shown great improvements in performance when processing image data owing to their powerful automatic feature learning and extraction abilities and are also widely used for HSI feature extraction and classification. The CNN extracts features based on the convolution operation. Nevertheless, the local perception of the convolution operation makes CNN focus on the local spectral features (LSF) and weakens the description of features between long-distance spectral ranges, which will be referred to as global spectral features (GSF) in this study. LSF and GSF describe the spectral features from two different perspectives and are both essential for determining the spectrum. Thus, in this study, a local-global spectral feature (LGSF) extraction and optimization method is proposed to jointly consider the LSF and GSF for HSI classification. To increase the relationship between spectra and the possibility to obtain features with more forms, we first transformed the 1D spectral vector into a 2D spectral image. Based on the spectral image, the local spectral feature extraction module (LSFEM) and the global spectral feature extraction module (GSFEM) are proposed to automatically extract the LGSF. The loss function for spectral feature optimization is proposed to optimize the LGSF and obtain improved class separability inspired by contrastive learning. We further enhanced the LGSF by introducing spatial relation and designed a CNN constructed using dilated convolution for classification. The proposed method was evaluated on four widely used HSI datasets, and the results highlighted its comprehensive utilization of spectral information as well as its effectiveness in HSI classification.

PCA-Domain Fused Singular Spectral Analysis for Fast and Noise-Robust Spectral–Spatial Feature Mining in Hyperspectral Classification

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral domain and spatial domain feature extraction in hyperspectral images (HSI). However, PCA itself suffers from low efficacy if no spatial information is combined, whilst 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this paper a PCA domain 2DSSA approach for spectral-spatial feature mining in HSI. Specifically, PCA and its variation, folded-PCA are utilized to fuse with the 2DSSA, as folded-PCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational complexity has been significantly reduced whilst preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, the proposed approach can work well on the uncorrected dataset without removing the noisy and water absorption bands, even under a small number of training samples. Experiments on two publicly available datasets have fully demonstrated the superiority of the proposed approach, in comparison to several state-of-the-art HSI classification methods and deep-learning models.

T-Linear Tensor Subspace Learning for Robust Feature Extraction of Hyperspectral Images

Subspace learning has been widely applied for feature extraction of hyperspectral images (HSIs) and achieved great success. However, the current methods still leave two problems that need to be further investigated. First, those methods mainly focus on finding one or multiple projection matrices for mapping the high-dimensional data into a low-dimensional subspace, which can only capture the information from each direction of high-order hyperspectral data separately. Second, the performance of feature extraction is barely satisfactory when the hyperspectral data is severely corrupted by noise. To address these issues, this article presents a t-linear tensor subspace learning (tLTSL) model for robust feature extraction of HSIs based on t-product projection. In the model, t-product projection is a new defined tensor transformation way similar to linear transformation in vector space, which can maximally capture the intrinsic structure of tensor data. The integrated tensor low-rank and sparse decomposition can effectively remove the noise corruption and the learned t-product projection can directly transform the high-order hyperspectral data into a subspace with information from all modes comprehensively considered. Moreover, a proposition related to tensor rank is proofed for interpreting the meaning of the tLTSL model. Extensive experiments are conducted on two different kinds of noise (i.e., simulated and real noise) corrupted HSI data, which validate the effectiveness of tLTSL.

Hyperspectral images feature extraction and classification based on fractional differentiation

Hyperspectral images feature extraction and classification based on fractional differentiation

An Improved 3D-2D Convolutional Neural Network Based on Feature Optimization for Hyperspectral Image Classification

As a new technology in the field of remote sensing, hyperspectral remote sensing has been widely used in land classification, mineral exploration, environmental monitoring, and other areas. In recent years, deep learning has achieved outstanding results in hyperspectral image classification tasks. However, problems such as low classification accuracy for small sample classes in unbalanced datasets and lack of robustness of the models usually lead to unstable classification performance of hyperspectral images. Therefore, from the perspective of feature optimization, we propose an improved hybrid convolutional neural network for hyperspectral image feature extraction and classification. Different from the current simple multi-scale feature extraction, we first optimize the features of each scale, and then perform multi-scale feature fusion. To this end, we use 3D dilated convolution to design a multi-level feature extraction block (MFB), which can be used to extract features with different correlation strengths at a fixed scale. Then, we construct a spatial multi-scale interactive attention (SMIA) module in the spatial feature enhancement phase, which can refine the multi-scale features through the attention weights of multi-scale feature interaction, and further improve the quality of spatial features. Finally, experiments were performed on different datasets, including balanced and unbalanced samples. The results show that the proposed model is more accurate and the extracted features are more robust.

Feature Extraction of Hyperspectral Images Based on Subspace Band Selection and Transform-Domain Recursive Filtering

During the feature extraction of hyperspectral images, a single filter cannot acquire complete information. To solve the problem, this paper proposes a feature extraction method based on subspace band selection and transform-domain recursive filtering. The proposed method contains three steps: Firstly, the target hyperspectral image is divided into multiple subsets of adjacent bands. Secondly, the Lasso-based band selection approach is adopted to compute the sparsity coefficient of each band. The bands in each subset are then ranked by the coefficient. Based on the ranking, the band with the highest coefficient is extracted from each subset, and used to reconstruct the hyperspectral data. Finally, the reconstructed hyperspectral image is processed through transform-domain recursive filtering, producing the features to be classified. Taking the support vector machine (SVM) as the classifier, our method was tested on several real hyperspectral image datasets. The results show that our method has a better classification accuracy than the other band selection methods.

Latent Low-Rank Projection Learning with Graph Regularization for Feature Extraction of Hyperspectral Images

Due to the great benefit of rich spectral information, hyperspectral images (HSIs) have been successfully applied in many fields. However, some problems of concern also limit their further applications, such as high dimension and expensive labeling. To address these issues, an unsupervised latent low-rank projection learning with graph regularization (LatLRPL) method is presented for feature extraction and classification of HSIs in this paper, in which discriminative features can be extracted from the view of latent space by decomposing the latent low-rank matrix into two different matrices, also benefiting from the preservation of intrinsic subspace structures by the graph regularization. Different from the graph embedding-based methods that need two phases to obtain the low-dimensional projections, one step is enough for LatLRPL by constructing the integrated projection learning model, reducing the complexity and simultaneously improving the robustness. To improve the performance, a simple but effective strategy is exploited by conducting the local weighted average on the pixels in a sliding window for HSIs. Experiments on the Indian Pines and Pavia University datasets demonstrate the superiority of the proposed LatLRPL method.

Spatial-spectral feature extraction of hyperspectral images for wheat seed identification

Hyperspectral images of wheat can identify seeds quickly, accurately, and nondestructively. However, most of the existing hyperspectral classification methods only use spectral information but ignore spatial information, resulting in unsatisfactory classification performance. To address these issues, we propose a spatial-spectral feature extraction method to identify seeds. Specifically, we first fuse the spatial and spectral features and then perform denoising. Subsequently, the principal component analysis is employed to extract features from the spatial-spectral data. Ultimately, the support vector machine trains and optimizes the model. Experimental results demonstrate that our method has the highest classification accuracy compared with the state-of-the-art methods. The classification accuracy of our method is achieved at 97.64% on the whole dataset. In addition, our method achieves better classification performance for small sample data.

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.

Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction

In the processing of remotely sensed data, classification may be preceded by feature extraction, which helps in making the most informative parts of the data emerge. Effective feature extraction may boost the efficiency and accuracy of the following classification, and hence various methods have been proposed to perform it. Recently, Singular Spectrum Analysis (SSA) and its 2-D variation (2D-SSA) have emerged as popular, cutting-edge technologies for effective feature extraction in Hyperspectral Images (HSI). Using 2D-SSA, each band image of an HSI is initially decomposed into various components, and then the image is reconstructed using the most significant eigen-tuples relative to their eigen-values, which represent strong spatial features for the classification task. However, instead of performing reconstruction on the whole image, it may be more effective to apply reconstruction to object-specific spatial regions, which is the proposed objective of this research. As an HSI may cover a large area, multiple objects are generally present within a single scene. Hence, spatial information can be highlighted accurately by specializing the reconstruction based on the local context. The local context may be defined by the so-called superpixels, i.e., finite sets of pixels that constitute a homogeneous set. Each superpixel may undergo tailored reconstruction, with a process expected to perform better than non-spatially-adaptive approaches. In this paper, a Superpixel-based SSA (SP-SSA) method is proposed where the image is first segmented into multiple regions using a superpixel segmentation approach. Next, each segment is individually reconstructed using 2D-SSA. In doing so, the spatial contextual information is preserved, leading to better classifier performance. The performance of the reconstructed features is evaluated using an SVM classifier. Experiments on four popular benchmark datasets reveal that, in terms of the classification accuracy, the proposed approach overperforms the standard SSA technique and various common spatio-spectral classification methods.

Spatial‐spectral feature extraction of hyperspectral images using tensor‐based collaborative graph analysis

Although the collaborative graph-based discriminant analysis (CGDA) method has shown promising performance for the feature extraction of the hyperspectral image (HSI), both the intrinsic local subspace structures and spatial structural information are ignored in CGDA. To address these problems, a novel spatial-spectral feature extraction method, i.e. tensor-based collaborative graph analysis, is proposed in this letter. Specifically, the spectral similarity is utilized to calculate the Tikhonov matrix, which can constrain the testing samples to be represented by similar training samples in the collaborative representation model. To fully exploit the 3D spatial-spectral structural information, the collaborative representation model is extended to tensor space by using the third-order tensor representation of HSI, in which samples are constructed by small local patches around the central pixels. Depending on the above techniques, the quality of the collaborative graph constructed by the coefficient matrix can be significantly improved to obtain the discriminative low-dimensional features. Experiments on three HSI data sets demonstrate the superiority of the proposed method.

Deep Manifold Structure-Preserving Spectral–Spatial Feature Extraction of Hyperspectral Image

The deep network has shown its superiority to extract discriminative features for hyperspectral image (HSI) classification. However, most existing methods only exploit label information of land classes to supervise the learning of deep features, and the learning process is totally automatic. Considering the complex spectral-spatial characteristic in real HSI, it is reasonable to utilize the manifold structure of input data to provide complementary information for precise classification. This article proposes a structure-preserving spectral-spatial network (SPSSN) to exploit the manifold structure information during the feature learning process, which can extract discriminative deep structure-preserving spectral-spatial features. Specifically, a basic spectral-spatial network (SSN), comprising a spectral module and a spatial module connected in series, is first built to learn spectral-spatial features from HSI cubes. Second, a novel structure-preserving constraint is introduced to transfer the intrinsic manifold structure of the input data into the learned deep spectral-spatial features, so as to enhance the discrimination. Furthermore, a feasible algorithm of imposing the constraint is designed to make it compatible with the deep learning framework, which results in a structure-preserving loss. Finally, the SSN is combined with the structure-preserving loss to obtain the SPSSN. Experiments on real HSI datasets verify the effectiveness and superiority of the SPSSN compared with several state-of-the-art methods in HSI classification.

Feature Extraction of Hyperspectral Image Based on Sparse Representation and Learning Graph Regularity

Feature Extraction of Hyperspectral Image Based on Sparse Representation and Learning Graph Regularity

Fusing Multiple Deep Models for In Vivo Human Brain Hyperspectral Image Classification to Identify Glioblastoma Tumor

Glioblastoma (GBM) tumor is the most common primary brain malignant tumor. The precise identification of GBM tumors is very important for diagnosis and treatment. Hyperspectral imaging is a fast, noncontact, accurate, and safe modern medical detection technology, which is expected to be a new tool of intraoperative diagnosis. In order to make full use of the spectral and spatial information of hyperspectral images (HSIs) to achieve accurate GBM tumor identification, a method based on the fusion of multiple deep models is proposed for in vivo human brain HSI classification. The proposed method includes the following major steps: 1) spectral phasor analysis and data oversampling; 2) 1-D deep neural network (1D-DNN)-based spectral HSI feature extraction and classification; 3) 2-D convolution neural network (2D-CNN)-based spectral–spatial HSI feature extraction and classification; 4) edge-preserving filtering-based classification result fusion and optimization; and 5) fully convolutional network (FCN)-based background segmentation. To verify the capabilities of the proposed method, experiments are performed on two real human brain hyperspectral datasets, including 36 in vivo HSIs captured from 16 different patients. The proposed method can achieve an overall accuracy of 96.69% for four-class classification and overall accuracy of 96.34% for GBM tumor identification. Experimental results demonstrate that the proposed method exhibits competitive classification performance and can generate satisfactory thematic maps of the location of the GBM tumor, which can provide the surgeon with guidance on successful and precise tumor resection.

Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures

Hyperspectral image classification has been increasingly used in the field of remote sensing. In this study, a new clustering framework for large-scale hyperspectral image (HSI) classification is proposed. The proposed four-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Initially, multidimensional Watershed is used for pre-segmentation. Region-based hierarchical hyperspectral image segmentation is based on the construction of Binary partition trees (BPT). Each segmented region is modeled while using first-order parametric modelling, which is then followed by a region merging stage using HSI regional spectral properties in order to obtain a BPT representation. The tree is then pruned to obtain a more compact representation. In addition, principal component analysis (PCA) is utilized for HSI feature extraction, so that the extracted features are further incorporated into the BPT. Finally, an efficient variant of k-means clustering algorithm, called filtering algorithm, is deployed on the created BPT structure, producing the final cluster map. The proposed method is tested over eight publicly available hyperspectral scenes with ground truth data and it is further compared with other clustering frameworks. The extensive experimental analysis demonstrates the efficacy of the proposed method.