Hyperspectral Image Processing Research Articles

Remote Sensing and GIS in Natural Resource Management: Comparing Tools and Emphasizing the Importance of In-Situ Data

Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales.

On-the-go table grape ripeness estimation via proximal snapshot hyperspectral imaging

The monitoring of grapes for ripeness estimation is a practice that enables fruit harvesting at the optimal time. Hyperspectral Imaging (HSI) represents a non-destructive and high-throughput alternative to traditional laboratory analyses. Current literature approaches perform hyperspectral measurements using line scan sensors or low-resolution static snapshot cameras, which hinder a fast per-bunch ripeness characterization. We propose a framework for on-the-go collection and processing of proximal snapshot hyperspectral images to estimate single bunch ripeness parameters. Focusing on table grapes (Vitis vinifera L. cv. Red Globe), we collected images under natural illumination with a hyperspectral camera (500–900 nm) mounted on a moving vehicle in an experimental block sited in Piacenza, Italy. We investigated images collected in August and September 2021 representing two ripening stages. The composition of the imaged grape bunches was determined through laboratory chemical analyses to predict Total Soluble Solids (TSS) and anthocyanin concentration. The images were pre-processed via multimodal image registration to correct the unalignment of bands due to the vehicle motion, and the single bunches were automatically identified on false RGB images through a Mask Region-Convolutional Neural Network (Mask R-CNN) instance segmentation network. The mean spectra of the bunches were used as input features of a Partial Least Squares Regression (PLSR) model to predict the chemical parameters at single bunch and whole vine scales. The regression model of TSS had an R2 (10-fold nested cross-validation) of 0.75 and 0.85 on a per-bunch and per-vine basis, respectively. The regression model of anthocyanin had an R2 of 0.68 and 0.49 on a per-bunch and per-vine basis, respectively. The results suggest the potential of using snapshot hyperspectral images for high-throughput analysis of a per-bunch grape ripeness estimation. The method described in this study could give valuable information to improve grape ripening monitoring and management of harvest operations and even allow for precise and automated robotic harvesting.

Hyperspectral Image Classification Algorithm for Forest Analysis Based on a Group-Sensitive Selective Perceptual Transformer

Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (PGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model’s spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model’s representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model’s versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markededly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing.

Hyperspectral image classification with token fusion on GPU

Hyperspectral images capture material nuances with spectral data, vital for remote sensing. Transformer has become a mainstream approach for tackling the challenges posed by high-dimensional hyperspectral data with complex structures. However, a major challenge they face when processing hyperspectral images is the presence of a large number of redundant tokens, which leads to a significant increase in computational load, adding to the model’s computational burden and affecting inference speed. Therefore, we propose a token fusion algorithm tailored to the operational characteristics of the hyperspectral image and pure transformer network, aimed at enhancing the final accuracy and throughput of the model. The token fusion algorithm introduces a token merging step between the attention mechanism and the multi-layer perceptron module in each Transformer layer. Experiments on four hyperspectral image datasets demonstrate that our token fusion algorithm can significantly improve inference speed without any training, while only causing a slight decrease in the pure transformer network’s classification accuracy.

Preface

Remote sensing technology and global positioning system (GPS), as the two pillars of modern space information technology, are changing the way we perceive and manage the Earth at an unprecedented speed. In order to promote academic exchanges and cooperation in this field, and to promote the transformation of scientific research and academic achievements, the 2024 International Conference on Remote Sensing and Global Positioning Algorithm (RSGPA 2024) was successfully held from June 28th to 30th, 2024 in Beijing, China via virtual form. The conference not only focused on the latest achievements of remote sensing mapping technology, but also discussed in depth the innovation and application of global positioning algorithms, aiming at promoting the transformation of scientific research and academic achievements through experience sharing and wisdom collision, promoting the gathering of talents, technology and capital, and injecting new kinetic energy into the development of related fields. It attracted a number of paper submissions related to remote sensing technology and global positioning system, and featured a variety of research directions, including Remote Sensing Mapping, Positioning Algorithms, AI-Assisted Localization, Hyperspectral Image Processing, Optical Remote Sensing, Microwave Remote Sensing, Remote Sensing Information Engineering, Image Processing Techniques, Geospatial Data Modeling and so on. The conference’s agenda covered a wide range of topics and presentations, mainly falling into keynote speeches, oral presentations, poster presentations, and academic discussion. Keynote speakers shared their latest research results and innovative ideas in the fields of remote sensing technology and global positioning system and their relevant domains, providing participants with valuable academic resources and inspiration. Professor Tao Chen (China University of Geosciences, Wuhan) talked about landslide recognition based on deep learning; Professor Muhammad Bilal (Henan Polytechnic University) presented a novel method for aerosol retrievals from multisensor remote sensing data over urban areas; Professor Kannimuthu Subramaniyam (Karpagam College of Engineering) illuminated the advancements and challenges in utilizing machine learning for cyber threat detection and mitigation. By fostering an environment of academic exchange and collaboration, RSGPA 2024 promoted the exploration of new ideas, the formation of partnerships, and the dissemination of research findings, ultimately contributing to the advancement of remote sensing and positioning technologies. All of the submissions received from the conference were exhaustively reviewed by the program committee members and peer-reviewers, who took into account the breadth and depth of the research topics that fall under the scope of RSGPA. The most promising contributions have been selected for inclusion in this Proceedings, which present innovative original ideas or results of general significance supported by clear and rigorous reasoning and compelling new evidence, as well as methods. This Proceedings not only provides participants with valuable academic resources, but also provides important references and insights for researchers in related fields. We would like to acknowledge all the speakers, authors, supporters, committee members and anonymous reviewers for their efforts in making RSGPA 2024 a complete success. We’d also appreciate the support from the staff of Journal of Physics: Conference Series for making this Proceedings published. The Committee of RSGPA 2024 List of Committee Member is available in this pdf.

SMALE: Hyperspectral Image Classification via Superpixels and Manifold Learning

As an extremely efficient preprocessing tool, superpixels have become more and more popular in various computer vision tasks. Nevertheless, there are still several drawbacks in the application of hyperspectral image (HSl) processing. Firstly, it is difficult to directly apply superpixels because of the high dimension of HSl information. Secondly, existing superpixel algorithms cannot accurately classify the HSl objects due to multi-scale feature categorization. For the processing of high-dimensional problems, we use the principle of PCA to extract three principal components from numerous bands to form three-channel images. In this paper, a novel superpixel algorithm called Seed Extend by Entropy Density (SEED) is proposed to alleviate the seed point redundancy caused by the diversified content of HSl. It also focuses on breaking the dilemma of manually setting the number of superpixels to overcome the difficulty of classification imprecision caused by multi-scale targets. Next, a space–spectrum constraint model, termed Hyperspectral Image Classification via superpixels and manifold learning (SMALE), is designed, which integrates the proposed SEED to generate a dimensionality reduction framework. By making full use of spatial context information in the process of unsupervised dimension reduction, it could effectively improve the performance of HSl classification. Experimental results show that the proposed SEED could effectively promote the classification accuracy of HSI. Meanwhile, the integrated SMALE model outperforms existing algorithms on public datasets in terms of several quantitative metrics.

Structural graph learning method for hyperspectral band selection

ABSTRACT Recently, graph learning-based hyperspectral band selection algorithms illustrate impressive performance for hyperspectral image (HSI) processing, whose goal is to select an optimal band combination containing less redundancy through the learned graph matrix. However, most of the previous methods work in single spectral domain, which neglects the rich image spatial information. Moreover, they typically model the graph matrix from a global perspective while ignoring the differences in spatial distribution that exist in diverse pixel and band regions. Based on the above considerations, to take full account of structure information in spatial and spectral domains, a structural graph learning method for hyperspectral band selection (SGLM) is designed. Specifically, SGLM constructs two matrices using the correlation between pixels and bands under the local perspective, which can capture the image structure information reasonably. Since the proposed method is modelled in both spatial and spectral dimensions, it is conducive to subsequent band subset generation task. Meanwhile, in order to guarantee local spatial consistency among bands, a Laplacian regularization term associated with the error matrix is introduced to the self-representation model. Additionally, considering that the importance of a band is consistent with its capability to reconstruct the entire band set, an adjacent bands reconstruction strategy is adopted to obtain the ultimate band combination, which can assess the significance of each band effectively. The comprehensive experimental analysis on four datasets demonstrates that the proposed SGLM model has outstanding superiority in comparison with several band selection algorithms.

Disentanglement-inspired single-source domain-generalization network for cross-scene hyperspectral image classification

Cross-scene classification stands as a pivotal frontier in hyperspectral image (HSI) processing, aiming to enhance the generalization capabilities of classification models. However, the diversity of sensor type, shooting environments, and shooting times leads to the spectral heterogeneity problem in HSI. As a result, the same land cover may exhibit varying spectral traits in different domains, posing challenges for cross-scene HSI classification. Drawing inspiration from image disentanglement, we have identified that extracting the latent domain-invariant representation (DIR) of HSI could potentially mitigate the spectral heterogeneity issue. Therefore, we propose a Disentanglement-Inspired Single-Source Domain Generalization Network (DSDGnet) for cross-scene HSI classification in this paper. Firstly, a style transfer module based on a Transformer encoder-transfer-decoder is designed to expand the single source domain to an extended domain. Then, a progressive disentanglement module is proposed to decompose the domain-invariant features and domain-specific features of HSI. Furthermore, a domain combination module is designed to guarantee the accuracy of the progressive disentanglement module and ensure the effectiveness of the domain-invariant feature of HSI. Finally, the domain-invariant features are applied to the classification task, and the domain-specific features are separated to reduce their impact on the generalization ability of classification models. Extensive experiments on three HSI datasets have demonstrated the advanced classification performance of DSDGnet compared to existing domain-generalization methods.

UAV hyperspectral image acquisition and processing, an application for nutrient estimation of rice in Vietnam

Hyperspectral imagery obtained from Unmanned Aerial Vehicles (UAVs) is increasingly employed to investigate nutrient concentrations in vegetation. The deployment of a hyperspectral camera on a UAV, flight planning, image acquisition, preprocessing of hyperspectral data, and the subsequent estimation of nutrient concentrations in vegetation are facing challenges. These challenges manifest as geometric, spectral distortions, and the abundance of numerous spectral bands. This study seeks to guide on mitigating the impact of issues encountered during an experiment to estimate nutrient concentrations in rice leaves using UAV hyperspectral images. An industrial hexagonal drone equipped with a push-broom hyperspectral camera featuring 122 bands within the Visible to Near-Infrared (VIS-NIR) wavelength range (400-960 nm) is employed to collect data over a 1-hectare testing rice field. Models for estimating Leaf Phosphorus Concentration (LPC) and Leaf Potassium Concentration (LKC) are developed based on the correlation between hyperspectral images, characterized by a 3 cm spatial resolution, and 162 LPC and 162 LKC reference data points. Utilizing various vegetation indices for LPC and LKC estimation, the outcomes reveal that a combination of band wavelengths at 838 nm and 734 nm is effective for LPC estimation, yielding a Root Mean Square Error (RMSE) of 27.1%. Conversely, LKC estimation exhibits an RMSE of 38.8% with an insignificant correlation between LKC and the current wavelength ranges. Above all, this study is a primary example of the utilization of UAV hyperspectral data in precision agriculture in Vietnam.

Hyperspectral Image Analysis Using Cloud-Based Support Vector Machines

Hyperspectral image processing techniques involve time-consuming calculations due to the large volume and complexity of the data. Indeed, hyperspectral scenes contain a wealth of spatial and spectral information thanks to the hundreds of narrow and continuous bands collected across the electromagnetic spectrum. Predictive models, particularly supervised machine learning classifiers, take advantage of this information to predict the pixel categories of images through a training set of real observations. Most notably, the Support Vector Machine (SVM) has demonstrate impressive accuracy results for image classification. Notwithstanding the performance offered by SVMs, dealing with such a large volume of data is computationally challenging. In this paper, a scalable and high-performance cloud-based approach for distributed training of SVM is proposed. The proposal address the overwhelming amount of remote sensing (RS) data information through a parallel training allocation. The implementation is performed over a memory-efficient Apache Spark distributed environment. Experiments are performed on a benchmark of real hyperspectral scenes to show the robustness of the proposal. Obtained results demonstrate efficient classification whilst optimising data processing in terms of training times.

Research and Implementation of FPGA Image Compression Parallel Algorithm Based on CCSDS

High-pixel, high-resolution spectral images are an important source of key graphical information obtained from space remote sensing, however, with the dramatically expanding amount of graphical information, the limited transmission bandwidth and hardware resources are difficult to meet the demand for massive data, which makes real-time image lossless compression and transmission technology an important solution. For this reason, this paper is based on the CCSDS image lossless compression algorithm, using FPGA for the construction of image compression system, and proposes a method strategy based on parallel acceleration algorithm to realize the real time processing of large-capacity hyperspectral images. The simulation results show that hyperspectral river and mountain range images can be compressed with greatly improved compression efficiency while maintaining real time performance and accuracy.

Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat

AbstractUncrewed aerial vehicles (UAVs) are a highly successful and efficient method for phenotyping in plant breeding programs. This study explored using UAVs equipped with hyperspectral sensors to expedite breeders' decision‐making in selecting winter wheat (Triticum aestivum L.) genotypes with improved growth, biomass, leaf area, and canopy cover (CC). The hyperspectral image processing pipeline utilized for image analysis was described. The study was conducted with 2145 genotypes of winter wheat, and UAV‐based hyperspectral measurements were used to predict the phenotype. Reflectance measurements were taken at narrow wavelength intervals, spanning 400–2500 nm. For ground truthing, samples were collected from different parts of the field. These samples were used to evaluate various plant attributes, including CC, leaf area index, plant height, and dry biomass. The hyperspectral data were employed for computation of multiple vegetation indices (VIs), and to improve the prediction of plant traits, we employed partial least squares regression (PLSR) and random forest (RF) regression techniques on both the complete set of hyperspectral variables and the top 10 derived VIs. Our results show that using complete hyperspectral variables results in superior r, R2, and lower root mean square error for both models. We conclude that relying solely on linear regression models with VIs may not always result in accurate predictions of plant traits in winter wheat. However, combining these indices with the RF and PLSR algorithm significantly enhances the prediction accuracy. However, the performance of both PLSR and RF models shows minimal disparity, with only slight differences observed. This highlights the importance of utilizing appropriate machine learning algorithms for improved prediction of plant traits.

Robust hyperspectral unmixing using weighted group sparsity and minimum volume regularization

ABSTRACT Hyperspectral unmixing (HU) has been a vital technique in hyperspectral image processing. Over the past years, many algorithms have been developed based on the nonnegative matrix factorization (NMF) framework. However, the conventional methods based on the NMF need prior information of the number of endmembers, which is usually estimated by a separate procedure. Therefore, the precision of estimating the number of endmembers will impact the subsequent unmixing effect. Thus, collaborating between the two processes is desired to avoid the propagation of errors. This paper proposes a new NMF-based method, which can estimate the number of endmembers and extract the endmembers and the abundance maps. Firstly, we introduce the weighted group sparsity regularization to adaptively eliminate the abundances corresponding to the redundant endmembers, while the abundances of the true endmembers are preserved. Then the number of endmembers can be determined by the reconstruction error function and the l 2 -norm of the abundance vectors. Secondly, since the simplex determined by the endmembers has the minimum volume among all simplexes that circumscribe the data points, we impose the total variational regularization to force the endmembers to be close to each other to minimize the simplex volume. And because the total variational regularization avoids the use of noisy pixels, it can decrease the influence of noise and improve the robustness of the solution. For the proposed model, we design an effective algorithm to solve it. Also, the experimental results based on the synthetic data and real data demonstrate that the proposed method can identify the endmembers number well and achieve robust unmixing results.

A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example.

Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.

DEVELOPMENT OF AEROSPACE IMAGES PRELIMINARY PROCESSING METHOD FOR SUBSEQUENT RECOGNITION AND IDENTIFICATION OF VARIOUS OBJECTS

Nowadays, the application of hyperspectral images is vital for every section of the humanity life such as agrotechnical research for the field condition state and water security. This article presents a new lossless data compression algorithm focused on the processing of hyperspectral aerospace images. The algorithm takes into account inter-band correlation and difference transformations to effectively reduce the range of initial values. correlation allows you to find the best reference channel that defines the sequence of operations in the algorithm, which contributes to a significant increase in the compression ratio while maintaining high data quality. The practical implementation of the algorithm lies in the process of the transfer the lower size file with high efficiency for unmanned aerial vehicle and satellites to save more computational resources. This method demonstrates high computational efficiency and can be applied to various tasks that require efficient storage and transmission of hyperspectral images. The importance of processing hyperspectral data and the problems associated with their volume and complexity of analysis were described. Current approaches to data compression are considered and their limitations are identified, which justifies the need to develop new methods. The relevance and necessity of effective compression algorithms for aerospace applications is emphasized. An analysis of existing methods and algorithms for compressing hyperspectral data was carried out. Particular attention is paid to approaches that use cross-channel correlation and difference transformations. The effectiveness of current methods is evaluated and their shortcomings are identified, which serves as a justification for the development of a new algorithm. A developed lossless data compression algorithm based on the use of inter-band correlation and difference transformations was described. The stages of forming groups of channels and the selection of optimal compression parameters are considered in detail. The method of determining the reference channel, which sets the sequence of operations in the algorithm, which provides more efficient data compression, is especially noted. The advantages and possible limitations of the new approach, as well as its potential for practical use, are discussed. It was noted that the developed method successfully solves the problems associated with the volume of hyperspectral data, providing a high compression ratio without quality loss. The prospects for further development of the algorithm and its application in various fields of science and technology are discussed.

A novel automatic Siamese neural network for hyperspectral image classification

Hyperspectral image is gradually becoming the main medium in satellite observations. It provides rich spectral information of different channels to help explore various gas states. Its classification problem is among the most significant tasks on the hyperspectral image processing and application. But the classification often faces the difficulty of imbalanced data and differentiated intra-class/inter-class difference. A lot of deep learning methods have attempted to solve this problem by designing different kinds of networks, but they suffer from the query of whether the network is optimal. This paper provides an automatically framework to deal with the above problem. First of all, we introduced siamese framework to reduce the impact of imbalanced data and various intra-class/inter-class difference. Then, we designed a multi-branch search space with several convolutional operators, especially a gradient-based updating rules to search the architecture. After using ”time [Formula: see text] performance” criterion, we obtain the model on two public hyperspectral image dataset. The experimental results outperformed all other compared algorithms.

Leveraging Seed Generation for Efficient Hardware Acceleration of Lossless Compression of Remotely Sensed Hyperspectral Images

In the field of satellite imaging, effectively managing the enormous volumes of data from remotely sensed hyperspectral images presents significant challenges due to the limited bandwidth and power available in spaceborne systems. In this paper, we describe the hardware acceleration of a highly efficient lossless compression algorithm, specifically designed for real-time hyperspectral image processing on FPGA platforms. The algorithm utilizes an innovative seed generation method for square root calculations to significantly boost data throughput and reduce energy consumption, both of which represent key factors in satellite operations. When implemented on the Cyclone V FPGA, our method achieves a notable operational throughput of 1598.67 Mega Samples per second (MSps) and maintains a power requirement of under 1 Watt, leading to an efficiency rate of 1829.1 MSps/Watt. A comparative analysis with existing and related state-of-the-art implementations confirms that our system surpasses conventional performance standards, thus facilitating the efficient processing of large-scale hyperspectral datasets, especially in environments where throughput and low energy consumption are prioritized.

A joint model based on graph and deep learning for hyperspectral anomaly detection

Through the years, graph theory has gradually been applied in hyperspectral image (HSI) processing. The graph theory method does not need to consider the structural characteristics of the dataset and has achieved satisfactory application results. However, the current graph theory methods applied in HSI processing mainly focus on spectral characteristics. This paper proposes a joint model based on graph and deep learning (JGD) for hyperspectral anomaly detection (AD). The proposed JGD consists of a spectral sub-model using graph and a spatial sub-model using deep learning. In the spectral sub-model, graph Fourier transform (GFT) is used, and the reconstruction errors between the original HSI and the HSI after GFT are mapped to fractional Fourier domain (FrFD) for AD results, which mainly makes use of the global spectral information. In the spatial sub-model, stacked autoencoders (SAEs) and an adaptive algorithm are combined to obtain AD results, which mainly use local spatial information and spectral information. These two sub-models are combined through the differential fusion method to form the JGD, which fully utilizes spectral and spatial information of hyperspectral images (HSIs). Compared with seven other algorithms on four real HSIs, the proposed JGD shows superior AD performance.

A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction

AbstractThis study demonstrates a new approach to process hyperspectral images where both the contextual spatial information as well as the spectral information are used to predict sample properties. The deep contextual spatial information is extracted using the deep feature extraction from pretrained resnet‐18 deep learning architecture, while the spectral information was readily available as the average pixel values. To fuse the information in a complementary way, a multiblock modeling approach called sequential orthogonalized partial least squares was used. The sequential model guarantees that the information learned is complementary from spatial and spectral domains. The potential of the approach is demonstrated to predict several physical and chemical properties in pork bellies.

Evaluation of Leaf Chlorophyll Content from Acousto-Optic Hyperspectral Data: A Multi-Crop Study

Chlorophyll plays a crucial role in the process of photosynthesis and helps to regulate plants’ growth and development. Timely and accurate evaluation of leaf chlorophyll content provides valuable information about the health and productivity of plants as well as the effectiveness of agricultural treatments. For non-contact and high-performance chlorophyll content mapping in plants, spectral imaging techniques are the most widely used. Due to agility and rapid random-spectral-access tuning, acousto-optical imagers seem to be very attractive for the detection of vegetation indices and chlorophyll content assessment. This laboratory study demonstrates the capabilities of an acousto-optic imager for evaluation of leaf chlorophyll content in six crops with different biophysical properties: Ribes rubrum, Betula populifolia, Hibiscus rosa-sinensis, Prunus padus, Hordeum vulgare and Triticum aestivum. The experimental protocol includes plant collecting, reference spectrophotometric measurements, hyperspectral imaging data acquisition, processing and analysis and building a multi-crop chlorophyll model. For 90 inspected samples of plant leaves, the optimal vegetation index and model were found. Obtained values of chlorophyll concentrations correlate well with reference values (determination coefficient of 0.89 and relative error of 15%). Applying a multi-crop model to each pixel, we calculated chlorophyll content maps across all plant samples. The results of this study demonstrate that acousto-optic imagery is very promising for fast chlorophyll content assessment and other laboratory spectral-index-based measurements.