Compact Band Weighting Module Based on Attention-Driven for Hyperspectral Image Classification

There is a relative paucity of studies that seek to nonparametrically describe or categorize hyperspectral image (HSI) data in the most natural context within which those data are best represented: n-dimensional (n-D) hyperspace, where n is the number of bands in an HSI data cube. Statistical techniques-and specifically those based on second-order statistics-are predominant and generally do not provide (nor are they utilized to provide or 'leave behind') an understanding of the distribution of spectra in hyperspace for the purpose of discovering patterns, comparing data sets, developing a deeper understanding of HSI data, etc. With the terabytes of HSI data in archives today representing a huge diversity of geographical settings, it seems natural to enquire as to whether there are universal patterns among HSI data sets. If such patterns exist, new techniques may be developed to exploit them, e.g., to enhance information extraction speed and accuracy. Since an understanding of the nature of HSI data has rarely gone beyond cursory, visual assessments of geographical setting; i.e., assessments of whether the data are of desert, forest, urban, littoral, etc., environments, it is perhaps worthwhile to develop methods to probe n-D hyperspace with the intent to identify and describe universal data commonalities, differences, classifications, and patterns. Building upon earlier work (Resmini, 2003 [1], 2006 [2]), an algorithm was created to describe the volume (and label the individual volume elements) of hyperspace occupied by an HSI data set. The algorithm essentially discretizes hyperspace assigning addresses to boxes in which data points (i.e., spectra) reside. The algorithm has been applied to visible/near-infrared to shortwave infrared (VNIR/SWIR) and longwave infrared (LWIR) HSI data cubes. Implemented in C code, the algorithm may also be used to calculate the fractal dimension of any array of points in any n-D hyperspace because it is essentially an implementation of box counting. The algorithm is described as are results of its application to actual HSI data sets. The fractal dimension of HSI data derived from the algorithm are also presented and compared to principal componentsbased estimates of data dimensionality. Application of the algorithm for developing a deeper understanding of the nature of HSI data as viewed as points in hyperspace is discussed as are practical applications for 'traditional' HSI exploitation activities such as anomaly and target detection.

Remote sensing Hyperspectral Image (HSI) comprises significant information about the earth’s surface which is actually acquired by hundred of narrow and adjacent spectral bands. The intended performance of classification accuracy does not attain due to the volume of the original HSI dataset and the enormous quantity of spectral bands. As such, dimensionality reduction approaches using feature extraction and selection are typically adopted to enhance classification performance. The unsupervised Principal Component Analysis (PCA), as well as the supervised Linear Discriminant Analysis (LDA), are commonly used as linear feature extraction methods for feature subspace detection. However, due to considering the effects of global variation, both PCA and LDA fail to extract local characteristics of HSI. In this paper, we propose a segmented LDA-based (SLDA) feature extraction where we apply the LDA in a segmented way to extract better local characteristics as well as global characteristics from the HSI. Per-pixel classification using a Support Vector Machine (SVM) is applied to our proposed SLDA method, PCA, Segmented-PCA (SPCA), and LDA on the Indian Pines agricultural HSI dataset. The experimental results show that the overall classification performance of SLDA (90.60%) remarkably outperforms all the other investigated methods: PCA (85.55%), SPCA (86.96%), LDA (86.45%), and the complete original dataset without employing any feature reduction method (83.10%). The proposed SLDA also requires the least amount of space complexity in different implementation scenarios.

Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been a major contributor to this trend. With a growing need for food safety measures and “smart applications” of insecticides, it is important to develop methods for rapid and accurate assessments of surface residues on food and feed items. As a model system, we investigated detection of a miticide applied to maize leaves and its miticidal bioactivity over time, and we compared two types of reflectance data: fourier transformed infrared (FTIR) data and hyperspectral imaging (HI) data. The miticide (bifenazate) was applied at a commercial field rate to maize leaves in the field, with or without application of a surfactant, and with or without application of a simulated “rain event”. In addition, we collected FTIR and HI from untreated control leaves (total of five treatments). Maize leaf data were collected at seven time intervals from 0 to 48 hours after application. FTIR data were analyzed using conventional analysis of variance of miticide-specific vibration peaks. Two unique FTIR vibration peaks were associated with miticide application (1,700 cm−1 and 763 cm−1). The integrated intensities of these two peaks, miticide application, surfactant, rain event, time between miticide application, and rain event were used as explanatory variables in a linear multi-regression fit to spider mite mortality. The same linear multi-regression approach was applied to variogram parameters derived from HI data in five selected spectral bands (664, 683, 706, 740, and 747 nm). For each spectral band, we conducted a spatial structure analysis, and the three standard variogram parameters (“sill”, “range”, and “nugget”) were examined as possible “indicators” of miticide bioactivity. We demonstrated that both FTIR peaks and standard variogram parameters could be used to accurately predict spider mite mortality, but linear multi-regression fits based on standard variogram parameters had the highest accuracy and were successfully validated with independent data. Based on experimental manipulation of HI data, the use of spatial structure analysis in classification of HI data was discussed.

Coupled adversarial learning for fusion classification of hyperspectral and LiDAR data

Deep learning has been a powerful tool for hyperspectral image (HSI) classification. However, it is still an open issue to effectively learn highly discriminative features from the HSI, due to the high-dimensionality and complex spectral-spatial characteristics. To settle this issue, we propose a new band-grouping guided multi-attention module for the performance promotion of spectral-spatial feature learning. First, based on the fact of high relevance between adjacent spectral bands and low dependencies across long-range ones, all the spectral bands are adaptively divided into multiple non-overlapping groups where relevant bands are included. The advantage is to reduce the spectral dimension and data complexity when processing and analyzing each group. Then, a multi-attention mechanism, which not only explore the intra-group salient information but also propagate the inter-group difference information, is embedded into the convolutional neural networks to learn group-specific spectral-spatial features. By emphasizing useful spectral/spatial information and squeezing useless information with attention mechanism, the severability of learned features is enhanced. Based on this module, a spectral-spatial classification network is built, named by grouped multi-attention network (GMA-Net). The GMA-Net contains a two-branch architecture, i.e., pixel-wise spectral feature learning and patch-wise spectral-spatial feature learning. Via fusing the features from two branches, the complementary and discriminative features provided by pixel-wise and patch-wise learning manner can be integrated to further boost classification performance. Experimental results demonstrate that the proposed method is superior than several state-of-the-art approaches. Codes are available at: https://github.com/luting-hnu.

Deep neural networks (DNNs) have revolutionized the way remotely sensed hyperspectral image (HSI) data are managed and processed. For instance, residual networks (ResNets) have achieved high classification accuracy by applying sequential transformations (layer by layer) on the input HSI data, obtaining highly discriminative data representations. However, these models are quite complex, with significant requirements in terms of memory resulting from the large number of parameters that they need to learn, which also leads to potential overfitting issues. In this work, we specifically address the aforementioned problem by re-interpreting a DNN (the ResNet) as a continuous transformation, instead of the traditional (discrete) step-by-step approach. To achieve this, we combine ordinary differential equations (ODEs) with DNN architectures for the first time in the HSI data classification literature. This allows us to perform remotely sensed HSI data classification in an efficient way in terms of number of parameters. Our experimental results, conducted using two well-known HSI data sets, indicate that the inclusion of ODEs in the architecture of DNNs offers significant advantages when processing and classifying this kind of high-dimensional data, achieving better performance even with less training data.

With the increasing number of remote sensing (RS) data sources, the joint utilization of multimodal data in Earth observation tasks has become a crucial research topic. As a typical representative of RS data, hyperspectral images (HSIs) provide accurate spectral information, while rich elevation information can be obtained from light detection and ranging (LiDAR) data. However, due to the significant differences in multimodal heterogeneous features, how to efficiently fuse HSI and LiDAR data remains one of the challenges faced by existing research. In addition, the edge contour information of images is not fully considered by existing methods, which can easily lead to performance bottlenecks. Thus, a joint classification network of HSI and LiDAR data based on Mamba (HLMamba) is proposed. Specifically, a gradient joint algorithm (GJA) is first performed on LiDAR data to obtain the edge contour data of the land distribution. Subsequently, a multimodal feature extraction module (MFEM) was proposed to capture the semantic features of HSI, LiDAR, and edge contour data. Then, to efficiently fuse multimodal features, a novel deep learning (DL) framework called Mamba, was introduced, and a multimodal Mamba fusion module (MMFM) was constructed. By efficiently modeling the long-distance dependencies of multimodal sequences, the MMFM can better explore the internal features of multimodal data and the interrelationships between modalities, thereby enhancing fusion performance. Finally, to validate the effectiveness of HLMamba, a series of experiments were conducted on three common HSI and LiDAR datasets. The results indicate that HLMamba has superior classification performance compared to other state-of-the-art DL methods. The source code of the proposed method will be available publicly at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Dilingliao/HLMamba</uri>.

Duct cancer is a malignant disease with higher mortality rates in males than in females, emphasizing the need for early diagnosis to improve treatment outcomes. Although various imaging modalities such as magnetic resonance imaging (MRI) and computed tomography scan (CT-scan) have been used for pathological analysis, hyperspectral imaging stands out as a promising approach, especially when combined with deep learning techniques. Hyperspectral imaging provides detailed information on tissue composition and biochemical properties, enabling better distinction between cancerous and healthy tissues. Although previous research based on hyperspectral imaging shows high accuracy, no previous research has used a small amount of training data, despite this being the usual case in medical image applications. Therefore, we propose a multi-path residual attention network (MRA-Net) with chunked residual channel attention (CRCA), which is a novel deep learning model specifically designed to address the challenges posed by limited training data, with a particular focus on using hyperspectral images. By leveraging the unique spectral information provided by hyperspectral imaging, MRA-Net extracts distinctive features, enhancing its ability to differentiate between cancerous and healthy tissues.We conducted the training and validation of our model using a publicly accessible dataset, resulting in an accuracy of 84.31% and a weighted harmonic mean of precision and recall (F1 score) of 84.29%, demonstrating its state-of-the-art performance compared to existing methods.

Hyperspectral image (HSI) clustering has been widely used in the field of remote sensing. However, most traditional clustering algorithms are not suitable for dealing with large-scale HSI due to their low clustering performance and high computational complexity. In this letter, we propose a novel fuzzy clustering algorithm, called fuzzy embedded clustering based on the bipartite graph (FECBG), to efficiently deal with large-scale HSI clustering problems. First, we propose the FECBG method that incorporates fuzzy clustering with the nonnegative regularization term based on the bipartite graph into a unified model, which has good clustering performance and reduces the sensitivity of fuzzy clustering to the initial cluster centers. Second, we adopt the fast spectral embedded method to obtain the low-dimensional representation of HSI data, to reduce the computational complexity. At last, we add the nonnegative regularization term based on the bipartite graph to fuzzy clustering, to constrain the solution space of the fuzzy membership matrix. Experimental results on several HSI data sets have demonstrated the efficiency and effectiveness of the proposed FECBG algorithm.

Hyperspectral image (HSI) classification remains a challenging problem due to unique characteristics of HSI data (such as numerous bands and strong correlations in the spectral and spatial domains) and small sample size. To address such concerns, we propose a novel spectral–spatial feature extraction method for HSI classification by employing graph embedding and deep learning (DL) models. Since the conventional graph cannot capture the complex manifold relationship of HSI data, and there exist the observations of within-class variation as well as the similarity between different classes in the spectral domain, we construct the supervised within-class/between-class hypergraph (SWBH) to extract the spectral features of HSI. Since it is difficult for DL models to learn representative features for HSI data when the labeled training samples are limited, we propose the random zero settings to newly generate a large amount of labeled HSI samples for the training of convolutional neural network (CNN). The designed sample expanded CNN (SECNN) is used to extract the HSI spatial features. Thus, the spectral–spatial features of HSI can be learned by integrating the features extracted from SWBH and SECNN, respectively. Experiments on three real HSI datasets demonstrate higher classification accuracy of the proposed SWBH–SECNN method.

Detection of moisture content in salted sea cucumbers by hyperspectral and low field nuclear magnetic resonance based on deep learning network framework

Hyperspectral image (HSI) clustering has attracted a great deal of attention, owing to lower cost and higher application prospects. Deep subspace clustering has been proved to be an effective method to explore the sample relationship of HSI clustering. However, due to the complex distribution of HSI data, the problem of data cluster overlap occurs frequently. In the actual sample distribution, a sample may belong to multiple subspaces. The complex sample distribution brings challenges to subspace clustering. In this letter, we propose a deep mutual information subspace clustering network (DMISC) to find a more intuitive feature space for non-linear subspace clustering. Technically, we maximize the mutual information between the samples and their generated features to enlarge the inter-class dispersion and intra-class compactness. The deep subspace method can find a more suitable non-linear intrinsic relationship, benefitting from the generated feature distribution. We evaluate DMISC on four HSI data sets and compare the performances with 12 popular clustering methods. The experiment results demonstrate our method outperforms many prior unsupervised methods.

Feature extraction (FE) methods based on low-rank representation (LRR) have become important topics in hyperspectral images (HSIs) data analysis. In this paper, a supervised FE method for HSIs data based on LRR with the ability to preserve the local pairwise constraints information (LRLPC) is proposed. LRLPC does not change the data dimensionality and only employs a technique to enrich the original feature space (OFS) and to obtain enriched feature space, which results in features richer than OFS. To overcome the problem of LRR in lacking the local structure information (LSI) of data, a local discriminative regularization term is imposed on the fitness function of LRR to keep the LSI of data. For nonlinear structure of data, LRLPC is extended to kernel LRLPC (KLRLPC) using kernel trick. Utilization of existing information in the pairwise constraints is useful for limited labeled samples situations as a common problem in HSI data analysis. The obtained experimental results using two well-known HSI data sets confirm the effectiveness of LRLPC and KLRLPC for dimension reduction and classification of HSIs.

Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging to analyze and store them within a reasonable digital infrastructure, especially in waste sorting where speed and data storage resources are limited. Additionally, as with most spectroscopic data, there is significant redundancy, making pixel and variable selection crucial for retaining chemical information. Recent high-tech developments in chemometrics enable automated and evidence-based data reduction, which can substantially enhance the speed and performance of Non-Negative Matrix Factorization (NMF), a widely used algorithm for chemical resolution of HSI data. By recovering the pure contribution maps and spectral profiles of distributed compounds, NMF can provide evidence-based sorting decisions for efficient waste management. To improve the quality and efficiency of data analysis on hyperspectral imaging (HSI) data, we apply a convex-hull method to select essential pixels and wavelengths and remove uninformative and redundant information. This process minimizes computational strain and effectively eliminates highly mixed pixels. By reducing data redundancy, data investigation and analysis become more straightforward, as demonstrated in both simulated and real HSI data for plastic sorting.

Hyperspectral images (HSI) have high-dimensional and complex spectral characteristics, with dozens or even hundreds of bands covering the same area of pixels. The rich information of the ground objects makes hyperspectral images widely used in satellite remote sensing. Due to the limitations of remote sensing satellite sensors, hyperspectral images suffer from insufficient spatial resolution. Therefore, utilizing software algorithms to improve the spatial resolution of hyperspectral images has become an urgent problem that needs to be solved. The spatial information and spectral information of hyperspectral images are strongly correlated. If only the spatial resolution is improved, it often damages the spectral information. Inspired by the high correlation between spectral information in adjacent spectral bands of hyperspectral images, a hybrid convolution and spectral symmetry preservation network has been proposed for hyperspectral super-resolution reconstruction. This includes a model to integrate information from neighboring spectral bands to supplement target band feature information. The proposed model introduces flexible spatial-spectral symmetric 3D convolution in the network structure to extract low-resolution and neighboring band features. At the same time, a combination of deformable convolution and attention mechanisms is used to extract information from low-resolution bands. Finally, multiple bands are fused in the reconstruction module, and the high-resolution hyperspectral image containing global information is obtained by Fourier transform upsampling. Experiments were conducted on the indoor hyperspectral image dataset CAVE, the airborne hyperspectral dataset Pavia Center, and Chikusei. In the X2 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 46.335, 36.321, and 46.310, respectively. In the X4 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 41.218, 30.377, and 38.365, respectively. The results show that our method outperforms many advanced algorithms in objective indicators such as PSNR and SSIM while maintaining the spectral characteristics of hyperspectral images.