Hyperspectral Data Processing Research Articles

Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions

IntroductionThe micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established.MethodsThe recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models.ResultsThe recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%.DiscussionThis study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.

Optimizing alteration mineral detection: A fusion of multispectral and hyperspectral remote sensing techniques in the Sar-e-Chah-e Shur, Iran

The Sar-e-Châh-e-Shur region in the Lut Block of eastern Iran has significant potential for diverse ore mineral deposits, particularly copper and iron mineralization. This study used multispectral remote sensing data, including ASTER, Sentinel 2, and Landsat 8 for detecting alteration zones associated with ore mineralization in the study area. Spectral ratios associated with porphyry copper-iron alterations and advanced argillic-iron alterations were utilized to establish training data, supplemented by hyperspectral data processing derived from Hyperion (spaceborne) and HyMap (airborne) sensors. Several classification algorithms were implemented, and evaluation metrics such as the Kappa coefficient and overall accuracy were calculated using a confusion matrix to assess classification performance. Field validation was conducted through thin mineral section analysis and geochemical stream sediment data, yielding a Normalized Score (NS) for conformity estimation. Notably, the Landsat 8, ASTER, and Sentinel 2 - classes category 2 -artificial neural networks-thresholds 2 (LAS-2-ANN2) class (representing advanced argillic alterations) was achieved a high score of 3.5, corresponding to an 87.5% match to the geological evidence. In addition, hyperspectral data processing using artificial neural network algorithms was yielded an overall score of 2.43, indicating a 61% match. In conclusion, this investigation highlighted the efficacy of fusing multispectral and hyperspectral data for accurate mineral alteration mapping in high potential regions, offering valuable insights for future exploration campaigns.

The role of topography feedbacks in enrichment of heavy metal elements in terrace type region

Minerals, metallurgy, and other production activities will cause a large number of heavy metal elements to leak into the natural environment. A large number of heavy metal elements have been found in the farmland soil, where the adsorption of plants enhances the enrichment. Here, we have selected a region with three terraces to conduct a whole-area soil sample collection and satellite hyperspectral data processing study to explore the role of terrain in this enrichment process. Five spectral transformation methods and four feature enhancement algorithms were designed, and the content extraction model was established to quantitatively retrieve eight heavy metal elements. The results indicates that the three terraces are the source state, transition state, and stable state of heavy metals respectively with the decrease of elevation; The correlation coefficient of various heavy metal elements exceeds 0.92, and the enrichment pattern is consistent although slope and aspect have no significant correlation with the enrichment of heavy metal elements; Local Cd exceeds 30.00%, Hg exceeds 10 times, and As exceeds 48.30% according to the indicator provisions of Chinese national standard (GB 15618-2018). Such knowledge extends our understanding of the abundance, migration, and enrichment of heavy metals from the perspective of topography, which is crucial for pollution assessment and soil remediation.

A Study on Dimensionality Reduction and Parameters for Hyperspectral Imagery Based on Manifold Learning.

With the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth's surface objects. However, the abundance of spectral information presents certain challenges for data processing, such as the "curse of dimensionality" leading to the "Hughes phenomenon", "strong correlation" due to high resolution, and "nonlinear characteristics" caused by varying surface reflectances. Consequently, dimensionality reduction of hyperspectral data emerges as a critical task. This paper begins by elucidating the principles and processes of hyperspectral image dimensionality reduction based on manifold theory and learning methods, in light of the nonlinear structures and features present in hyperspectral remote-sensing data, and formulates a dimensionality reduction process based on manifold learning. Subsequently, this study explores the capabilities of feature extraction and low-dimensional embedding for hyperspectral imagery using manifold learning approaches, including principal components analysis (PCA), multidimensional scaling (MDS), and linear discriminant analysis (LDA) for linear methods; and isometric mapping (Isomap), locally linear embedding (LLE), Laplacian eigenmaps (LE), Hessian locally linear embedding (HLLE), local tangent space alignment (LTSA), and maximum variance unfolding (MVU) for nonlinear methods, based on the Indian Pines hyperspectral dataset and Pavia University dataset. Furthermore, the paper investigates the optimal neighborhood computation time and overall algorithm runtime for feature extraction in hyperspectral imagery, varying by the choice of neighborhood k and intrinsic dimensionality d values across different manifold learning methods. Based on the outcomes of feature extraction, the study examines the classification experiments of various manifold learning methods, comparing and analyzing the variations in classification accuracy and Kappa coefficient with different selections of neighborhood k and intrinsic dimensionality d values. Building on this, the impact of selecting different bandwidths t for the Gaussian kernel in the LE method and different Lagrange multipliers λ for the MVU method on classification accuracy, given varying choices of neighborhood k and intrinsic dimensionality d, is explored. Through these experiments, the paper investigates the capability and effectiveness of different manifold learning methods in feature extraction and dimensionality reduction within hyperspectral imagery, as influenced by the selection of neighborhood k and intrinsic dimensionality d values, identifying the optimal neighborhood k and intrinsic dimensionality d value for each method. A comparison of classification accuracies reveals that the LTSA method yields superior classification results compared to other manifold learning approaches. The study demonstrates the advantages of manifold learning methods in processing hyperspectral image data, providing an experimental reference for subsequent research on hyperspectral image dimensionality reduction using manifold learning methods.

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.

Estimation of the Relative Chlorophyll Content of Carya illinoensis Leaves Using Fractional Order Derivative of Leaf and Canopy Scale Hyperspectral Data

Chlorophyll is a crucial physiological and biochemical indicator that impacts plant photosynthesis, accumulation of photosynthetic products, and final yield. The measurement and analysis of chlorophyll content in plants can provide valuable insights into their nutritional status and overall health. The non-destructive and efficient estimation of relevant plant physiological and biochemical indicators using hyperspectral technology can provide a reliable method for collecting data on nutrient levels and health status during plant growth and development. Fifty-three Carya illinoensis plants of Jiande and Changlin series known for their exceptional qualities and significant economic benefits were used as the research object for collecting their leaf and canopy hyperspectral data. Firstly, fractional order derivative (FOD) was used for spectral preprocessing. Secondly, the spectral response relationship between spectrum and relative chlorophyll content (soil and plant analyzer development, SPAD) was explored by combining single-band and two-band spectral index (normalized difference spectral index, NDSI). The correlation coefficient of Pearson correlation analysis was used to estimate the linear correlation between variables. Finally, the correlation between the spectral feature variables and SPAD was analyzed and calculated. Top 10 absolute values of the correlation coefficients were screened out as modeling variables. eXtreme gradient boosting (XGBoost) machine learning algorithm was used to construct the optimal estimation model of SPAD of Carya illinoensis leaves. Results showed that the correlation between leaf and canopy spectrum after FOD pretreatment and SPAD was substantially improved, compared with raw spectrum. FOD combined with leaf and canopy NDSI was more effective than single band in improving the correlation between spectral characteristics and target components, which was increased by 0.166 and 0.338, respectively. The leaf spectrum could estimate SPAD more accurately than that of canopy spectrum. The optimal SPAD model was the 0.5th-order derivative transformation combined with two-band leaf spectral index (NDSI) model. The R2 P was 0.788, and the RMSEP was 0.842 in prediction set. On one hand, this study confirms the feasibility of rapid and non-destructive estimation of SPAD of Carya illinoensis leaves using hyperspectral technology. On the other hand, FOD combined with two-band spectral indices can significantly improve the response relationship between spectral characteristics and target variables, enrich hyperspectral data processing methods, and propose a novel approach for the detection of plant nutrient level and health.

Information about the book by Joseph Dubrovkin “Data Compression IN Spectroscopy”, Cambridge Scholars Publishing. 2022, 355 PP. ISBN (10): 1-5275-8620-0 ISBN (13)

Widespread use of instrumental methods of analysis, in particular analytical spectroscopy, in recent years has led to increasing volume of information about the objects being studied. Experimental results are stored on a computer in the form of multidimensional digital data arrays that need to be processed in a special way, for example, to remove noise, visualize, analyze, and compress for efficient storage. For these purposes, there are theoretical methods and algorithms of data compression. At the same time compression (decompression) algorithms must ensure minimum distortion of the original signals for processing hyperspectral data. Large volume of data implies a comprehensive analysis of information. When working with multidimensional arrays, it is important to use special methods and technologies for processing, analyzing and interpreting hyperspectral data. This article provides information about the monograph “Data Compression in Spectroscopy” by Joseph Dubrovkin. The book was published by Cambridge Scholars Publishing. The book was written by an expert with extensive experience in the field of multivariate data analysis and chemometrics. The book consists of a preface, information about the structure of the book, a list of abbreviations and symbols, an introduction for each of 4 chapters, 8 appendices, a list of references and a subject index. A large number of examples and exercises are illustrated with MATLAB programs, and bibliographic tables clearly demonstrate the use of compression methods in industrial and research laboratories. The material in the book is discussed chapter by chapter. This modern monograph on data compression in spectroscopy will be useful as a teaching aid for students and teaching staff, as well as for specialists in analytical laboratories.

A Novel Correction Methodology to Improve the Performance of a Low-Cost Hyperspectral Portable Snapshot Camera.

The development of spectral sensors (SSs) capable of retrieving spectral information have opened new opportunities to improve several environmental and agricultural practices, e.g., crop breeding, plant phenotyping, land use monitoring, and crop classification. The SSs are classified as multispectral and hyperspectral (HS) based on the number of the spectral bands resolved and sampled during data acquisition. Large-scale applications of the HS remain limited due to the cost of this type of technology and the technical difficulties in hyperspectral data processing. Low-cost portable hyperspectral cameras (PHCs) have been progressively developed; however, critical aspects associated with data acquisition and processing, such as the presence of spectral discontinuities, signal jumps, and a high level of background noise, were reported. The aim of this work was to analyze and improve the hyperspectral output of a PHC Senop HSC-2 device by developing a general use methodology. Several signal gaps were identified as falls and jumps across the spectral signatures near 513, 650, and 930 nm, while the dark current signal magnitude and variability associated with instrumental noise showed an increasing trend over time. A data correction pipeline was successfully developed and tested, leading to 99% and 74% reductions in radiance signal jumps identified at 650 and 830 nm, respectively, while the impact of noise on the acquired signal was assessed to be in the range of 10% to 15%. The developed methodology can be effectively applied to other low-cost hyperspectral cameras.

Hyperspectral imaging – a short review of methods and applications

This paper takes a look at the state-of-the-art solutions in the field of spectral imaging systems by way of application examples. It is based on a comparison of currently used systems and the challenges they face, especially in the field of high-altitude imaging and satellite imaging, are discussed. Based on our own experience, an example of hyperspectral data processing is presented. The article also discusses how modern algorithms can help in understanding the data that such images can provide.

LSCA-net: A lightweight spectral convolution attention network for hyperspectral image processing

The application of hyperspectral imaging with computer-aided technology has promising prospects, and achieving real-time, efficient, and non-destructive detection, especially for food and agricultural products, undoubtedly poses a great challenge. Hyperspectral data processing has many complications, such as large volume, high redundancy, and difficulty extracting useful features. Therefore, this study develops a lightweight end-to-end unified framework for deep neural networks with excellent generalization to conserve memory space and computation. To improve the classification accuracy and performance of the core model LSAC-net, we combine an attention mechanism based on the temporal convolution method with a complementary model-scaling technique. Experimental results on our own datasets for the production year of citri reticulate pericarpium, production origin of Pu’er tea, and process mode of coffee beans show that our model outperforms several other state-of-the-art models.

A Neural Network for Hyperspectral Image Denoising by Combining Spatial–Spectral Information

Hyperspectral imaging often suffers from various types of noise, including sensor non-uniformity and atmospheric disturbances. Removing multiple types of complex noise in hyperspectral images (HSIs) while preserving high fidelity in spectral dimensions is a challenging task in hyperspectral data processing. Existing methods typically focus on specific types of noise, resulting in limited applicability and an inadequate ability to handle complex noise scenarios. This paper proposes a denoising method based on a network that considers both the spatial structure and spectral differences of noise in an image data cube. The proposed network takes into account the DN value of the current band, as well as the horizontal, vertical, and spectral gradients as inputs. A multi-resolution convolutional module is employed to accurately extract spatial and spectral noise features, which are then aggregated through residual connections at different levels. Finally, the residual mixed noise is approximated. Both simulated and real case studies confirm the effectiveness of the proposed denoising method. In the simulation experiment, the average PSNR value of the denoised results reached 31.47 at a signal-to-noise ratio of 8 dB, and the experimental results on the real data set Indian Pines show that the classification accuracy of the denoised hyperspectral image (HSI) is improved by 16.31% compared to the original noisy version.

Individual Tree Species Identification and Crown Parameters Extraction Based on Mask R-CNN: Assessing the Applicability of Unmanned Aerial Vehicle Optical Images

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and spectral features. As such, they have attracted extensive attention in the field of forest resource investigation, but their applicability for end-to-end individual tree species identification is unclear. Based on the Mask R-CNN instance segmentation model, this study utilized UAV hyperspectral images to generate spectral thinning data, spectral dimensionality reduction data, and simulated multispectral data, thereby evaluating the importance of high-resolution spectral information, the effectiveness of PCA dimensionality reduction processing of hyperspectral data, and the feasibility of multispectral data for individual tree identification. The results showed that the individual tree species identification accuracy of spectral thinning data was positively correlated with the number of bands, and full-band hyperspectral data were better than other hyperspectral thinning data and PCA dimensionality reduction data, with Precision, Recall, and F1-score of 0.785, 0.825, and 0.802, respectively. The simulated multispectral data are also effective in identifying individual tree species, among which the best result is realized through the combination of Green, Red, and NIR bands, with Precision, Recall, and F1-score of 0.797, 0.836, and 0.814, respectively. Furthermore, by using Green–Red–NIR data as input, the tree crown area and width are predicted with an RMSE of 3.16m2 and 0.51m, respectively, along with an rRMSE of 0.26 and 0.12. This study indicates that the Mask R-CNN model with UAV optical images is a novel solution for identifying individual tree species and extracting crown parameters, which can provide practical technical support for sustainable forest management and ecological diversity monitoring.

Low-Cost Hyperspectral Imaging Device for Portable Remote Sensing

Hyperspectral imaging has revolutionized various scientific fields by enabling a detailed analysis of objects and materials based on their spectral signatures. However, the high cost and complexity of commercial hyperspectral camera systems limit their accessibility to researchers and professionals. In this paper, a do-it-yourself (DIY) hyperspectral camera device that offers a cost-effective and user-friendly alternative to hyperspectral imaging is presented. The proposed device leverages off-the-shelf components, commercially available hardware parts, open-source software, and novel calibration techniques to capture and process hyperspectral imaging data. The design considerations, hardware components, and construction process are discussed, providing a comprehensive guide for building the device. Furthermore, the performance of the DIY hyperspectral camera is investigated through experimental evaluations with a multi-color 3D-printed box in order to validate its sensitivities to red, green, blue, orange and white colors.

Construction of multidimensional features to identify tea plantations using multisource remote sensing data: A case study of Hangzhou city, China

China is the world's largest tea producer and trader. From 1995 to 2020, the plantation area and tea production increased by approximately 200 and 400%, respectively. Therefore, we must accurately identify the distribution of tea plantations to optimize production and promote sustainable development. However, most studies on the identification of tea plantations rely on local hyperspectral data processing, which have limitations related to a small scale, significant time consumption, and difficulty in integrating multisource images and multidimensional features to achieve the accuracy and optimization of the results. Based on the Google Earth Engine (GEE) platform, we integrated the spectral, synthetic aperture radar (SAR), terrain, and textural features to extract the seasonal phenological index using specific phenological information on tea plantations. Then, the random forest (RF) algorithm was applied to evaluate the importance of each feature in the initial feature set, followed by screening of the optimal feature combination and verification of the Jeffries–Matusita (JM) distance. Finally, the optimal feature combination was used as the input variable for the RF classification algorithm to identify the distribution of tea plantations in Hangzhou, China. The following outcomes were achieved: (1) compared to the processing of local hyperspectral data, the identification of tea plantations using the GEE had notable advantages, such as high automation, convenient algorithm adjustment, and rapid iteration of the results. (2) The user and producer accuracies of the classification of tea plantations using SAR and terrain and textural features improved from 89.3 and 85.9% to 95.8 and 91.7%, respectively, compared to only using spectral information. (3) The high-contribution feature combination identified by the importance analysis using the RF algorithm increased the JM distance to a high degree of separability of approximately 2, which is highly effective for the optimization of classification features. (4) In addition to the visible and near-infrared bands commonly used in spectral information, short-wave infrared and vegetation red-edge bands can play important roles in identifying tea plantations. (5) The importance of the same spectral or SAR information varied for different seasonal phenological indicators, indicating that the addition of phenological information was beneficial for the identification of tea plantations.

Hyperspectral inversion of soil water and salt information based on fractional order derivative technology.

Accurate and efficient acquisition of soil water and salt information is a prerequisite for the improvement and sustainable utilization of saline lands. With the ground field hyperspectral reflectance and the measured soil water-salt content as data sources, we used the fractional order differentiation (FOD) technique to process hyperspectral data (with a step length of 0.25). The optimal FOD order was explored at the correlation level of spectral data and soil water-salt information. We constructed two-dimensional spectral index, support vector machine regression (SVR) and geographically weighted regression (GWR). The inverse model of soil water-salt content was finally evaluated. The results showed that FOD technique could reduce the hyperspectral noise and explore the potential spectral information to a certain extent, improve the correlation between spectrum and characteristics, with the highest correlation coefficients of 0.98, 1.35 and 0.33. The combination of characteristic bands screened by FOD and two-dimensional spectral index were more sensitive to characteristics than one-dimensional bands, with the optimal responses of order 1.5, 1.0 and 0.75. The optimal combinations of bands for maximum absolute correction coefficient of SMC were 570, 1000, 1010, 1020, 1330 and 2140 nm, pH were 550, 1000, 1380 and 2180 nm, and salt content were 600, 990, 1600 and 1710 nm, respectively. Compared with the original spectral reflectance, the validation coefficients of determination (Rp2) of the optimal order estimation models for SMC, pH, and salinity were improved by 1.87, 0.94 and 0.56, respectively. The overall GWR accuracy in the proposed model was better than SVR, where the GWR optimal order estimation models Rp2 were 0.866, 0.904 and 0.647, and the relative per-centage difference were 3.54, 4.25 and 1.86, respectively. The overall spatial distribution of soil water and salt content in the study area was characterized by low in the west and high in the east, with more serious soil alkalinization problems in the northwest and less severe in the northeast. The results would provide scientific basis for the hyperspectral inversion of soil water and salt in the Yellow River Irrigation Area and a new strategy for the implementation and management of precision agriculture in saline soil areas.

Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model

Improvements in hyperspectral image technology, diversification methods, and cost reductions have increased the convenience of hyperspectral data acquisitions. However, because of their multiband and multiredundant characteristics, hyperspectral data processing is still complex. Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. The optimal classification model was obtained by comparing a stacked autoencoder (SAE) and a deep belief network (DBN). Finally, the SAE was further optimized by adding sparse representation constraints and GPU parallel computation to improve classification accuracy and speed. The research results show that the SAE enhanced by deep learning is superior to the traditional feature extraction algorithm. The optimal classification model based on deep learning, namely, the stacked sparse autoencoder, achieved 93.41% and 94.92% classification accuracy using two experimental datasets. The use of parallel computing increased the model’s training speed by more than seven times, solving the model’s lengthy training time limitation.

Detection and Identification of Potato-Typical Diseases Based on Multidimensional Fusion Atrous-CNN and Hyperspectral Data

As one of the world’s most crucial crops, the potato is an essential source of nutrition for human activities. However, several diseases pose a severe threat to the yield and quality of potatoes. Timely and accurate detection and identification of potato diseases are of great importance. Hyperspectral imaging has emerged as an essential tool that provides rich spectral and spatial distribution information and has been widely used in potato disease detection and identification. Nevertheless, the accuracy of prediction is often low when processing hyperspectral data using a one-dimensional convolutional neural network (1D-CNN). Additionally, conventional three-dimensional convolutional neural networks (3D-CNN) often require high hardware consumption while processing hyperspectral data. In this paper, we propose an Atrous-CNN network structure that fuses multiple dimensions to address these problems. The proposed structure combines the spectral information extracted by 1D-CNN, the spatial information extracted by 2D-CNN, and the spatial spectrum information extracted by 3D-CNN. To enhance the perceptual field of the convolution kernel and reduce the loss of hyperspectral data, null convolution is utilized in 1D-CNN and 2D-CNN to extract data features. We tested the proposed structure on three real-world potato diseases and achieved recognition accuracy of up to 0.9987. The algorithm presented in this paper effectively extracts hyperspectral data feature information using three different dimensional CNNs, leading to higher recognition accuracy and reduced hardware consumption. Therefore, it is feasible to use the 1D-CNN network and hyperspectral image technology for potato plant disease identification.

Recognition of Abnormal-Laying Hens Based on Fast Continuous Wavelet and Deep Learning Using Hyperspectral Images

The egg production of laying hens is crucial to breeding enterprises in the laying hen breeding industry. However, there is currently no systematic or accurate method to identify low-egg-production-laying hens in commercial farms, and the majority of these hens are identified by breeders based on their experience. In order to address this issue, we propose a method that is widely applicable and highly precise. First, breeders themselves separate low-egg-production-laying hens and normal-laying hens. Then, under a halogen lamp, hyperspectral images of the two different types of hens are captured via hyperspectral imaging equipment. The vertex component analysis (VCA) algorithm is used to extract the cockscomb end member spectrum to obtain the cockscomb spectral feature curves of low-egg-production-laying hens and normal ones. Next, fast continuous wavelet transform (FCWT) is employed to analyze the data of the feature curves in order to obtain the two-dimensional spectral feature image dataset. Finally, referring to the two-dimensional spectral image dataset of the low-egg-production-laying hens and normal ones, we developed a deep learning model based on a convolutional neural network (CNN). When we tested the model's accuracy by using the prepared dataset, we found that it was 0.975 percent accurate. This outcome demonstrates our identification method, which combines hyperspectral imaging technology, an FCWT data analysis method, and a CNN deep learning model, and is highly effective and precise in laying-hen breeding plants. Furthermore, the attempt to use FCWT for the analysis and processing of hyperspectral data will have a significant impact on the research and application of hyperspectral technology in other fields due to its high efficiency and resolution characteristics for data signal analysis and processing.

Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing: A summary of current methods

Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpretation. However they may not be suitable for analyzing scenes with unknown complex physical characteristics. Data-driven methods have developed rapidly in recent years, in particular deep learning methods because they possess superior capability in modeling complex and nonlinear systems. Simply transferring these methods as black-boxes to conduct unmixing may lead to low physical interpretability and generalization ability. This article reviews hyperspectral unmixing works that integrate advantages of both physics-based models and data-driven methods by means of deep neural network structures design, prior design and loss design. Most of these methods derive from a common mathematical optimization framework, and combine good interpretability with high accuracy.

Improvement of pixel classification by the simultaneous use of spectral and spatial information in the framework of spectroscopic imaging

Hyperspectral imaging technology is developing in a very fast way. We find it today in many analytical developments using different spectroscopies for sample classification purposes. Instrumental developments allow us to acquire more and more data in shorter and shorter periods of time while improving their quality. Therefore, we are going in the right direction as far as the measure is concerned. On the other hand, we can make a more mixed assessment for the hyperspectral imaging data processing. Indeed, the data acquired in spectroscopic imaging have the particularity of encoding both spectral and spatial information. Unfortunately, in chemometrics, almost all classification approaches today only use spectral information from three-dimensional hyperspectral data arrays. To be more precise, an approach encompassing the unfolding/refolding of such arrays is often applied beforehand because the majority of algorithms for analysing these data are not capable of handling them in their original structure. Spatial information is therefore lost during the chemometric exploration. The study of the spectral part of the acquired data array alone is clearly a limitation that we propose to overcome in this work. 2-D Stationary Wavelet Transform will be used in the data preprocessing phase to ensure the joint use of spectral and spatial information. Two spectroscopic datasets will then be used to evaluate the potential of our approach in the context of supervised classification.