Hyperspectral Imagery Research Articles

CoastalWQL: An Open-Source Tool for Drone-Based Mapping of Coastal Turbidity Using Push Broom Hyperspectral Imagery

Uncrewed-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as effective alternatives for monitoring water quality on-demand. However, image mosaicking for largely featureless coastal water surfaces or open seas has shown to be challenging. Another pertinent issue observed is the systematic image misalignment between adjacent flight lines due to the time delay between the UAV-borne sensor and the GNSS system. To overcome these challenges, this study introduces a workflow that entails a GPS-based image mosaicking method for push-broom hyperspectral images, together with a correction method to address the aforementioned systematic image misalignment. An open-source toolkit, CoastalWQL, was developed to facilitate the workflow, which includes essential pre-processing procedures for improving the image mosaic’s quality, such as radiometric correction, de-striping, sun glint correction, and object masking classification. For validation, UAV-based push-broom hyperspectral imaging surveys were conducted to monitor coastal turbidity in Singapore, and the implementation of CoastalWQL’s pre-processing workflow was evaluated at each step via turbidity retrieval. Overall, the results confirm that the image mosaicking of the push-broom hyperspectral imagery over featureless water surface using CoastalWQL with time delay correction enabled better localisation of the turbidity plume. Radiometric correction and de-striping were also found to be the most important pre-processing procedures, which improved turbidity prediction by 46.5%.

A model for forest type identification and forest regeneration monitoring based on deep learning and hyperspectral imagery

Traditional ground-based forest survey methods involve high labor costs, and their inefficiency makes comprehensive forest resource surveys challenging. With the development of new sensors and vehicles in recent years, more diverse and novel remote sensing detection and survey techniques have emerged. This study aims to use hyperspectral imagery to classify forest types containing representative tree species. To verify the feasibility of the proposed methods, we used hyperspectral imagery from the Taiwan Forestry Experiment Institute's Liugui Research Forest in southern Taiwan, which has an area of 9882 ha and an altitude of 250–2600 m. Hyperspectral imagery offers several advantages compared to traditional multispectral imagery; it captures a broad spectrum of contiguous, narrow spectral bands, providing highly detailed spectral information, enabling differentiation of tree components that appear similar in multispectral imagery.Eight identifiable forest types were selected for the models considered, and three different deep learning algorithms, VGG19, ResNet50 and a proposed combination (VGG19 + ResNet50), were used to screen the best algorithms. Data formats and pre-processing methods that can effectively improve computational performance were explored. The research results found that: (1) Band filtering is a necessary means to improve calculation performance; (2) Flattening the original convolution kernel with cubic characteristics can significantly reduce the time required for calculation. In terms of simulation results, VGG19 + ResNet50 was identified as the best model. Its overall classification accuracy can generally reach 93% to 100%. According to the calculation process set in this study, the time required for model training can be shortened to less than 30 min. The results of this research will help process more detailed and complex information in forest resource management and more accurately quantify forest ecology and woodland conditions.

Bitou bush detection and mapping using UAV-based multispectral and hyperspectral imagery and artificial intelligence

The use of Unmanned Aerial Vehicles (UAVs) for remote sensing (RS) of vegetation presents a valuable platform for weed monitoring, owing to the high spatial resolution of collected images. Accurate segmentation and mapping of weed spatial distribution plays a pivotal role in achieving effective management and ensures efficient and sustainable utilization of weed control measures. Furthermore, UAV-based RS provides a rapid way of assessing phenological development stages of weed species such as flowering and fruiting. These are often critical stages required for the separation of weed species from surrounding vegetation and are difficult to capture with traditional low resolution airborne and satellite RS imagery. Bitou bush is a shrub and a serious environmental weed of coastal areas of New South Wales (NSW). The primary objective of this study is to develop a model for bitou bush mapping from collected multispectral (MS) and hyperspectral (HS) imagery on location in NSW, Australia by employing various classical machine learning (ML) and deep learning (DL) techniques. The performance of Random forests (RF), Support vector machine (SVM), Extreme gradient boosting (XGB), and K-nearest neighbors (KNN) models is evaluated, achieving overall validation accuracies of 78%, 74%, 80%, and 69%, respectively for bitou bush detection using MS imagery. Subsequently, these models are assessed on HS data, resulting in overall validation accuracies of 77%, 86%, 86%, and 80% for RF, SVM, XGB, and KNN, respectively. Moreover, the DL U-Net model achieved an overall validation accuracy of 92%, outperforming the classical ML models in MS data segmentation tasks. The results of this study highlight the superior performance of the U-Net model in comparison to classical ML models in RS data segmentation, indicating the value of DL techniques for more accurate and robust RS applications such as bitou bush detection and mapping. The insights gained from this research will aid researchers and land managers select appropriate models based on the complexity and characteristics of their RS datasets. Moreover, the integration of UAV RS and artificial intelligence (AI) provide a valuable and efficient platform for bitou bush monitoring and management practices, ultimately enhancing the efficiency and sustainability of weed control efforts.

A Joint Convolutional Cross ViT Network for Hyperspectral and Light Detection and Ranging Fusion Classification

The fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data for classification has received widespread attention and has led to significant progress in research and remote sensing applications. However, existing common CNN architectures suffer from the significant drawback of not being able to model remote sensing images globally, while transformer architectures are not able to capture local features effectively. To address these bottlenecks, this paper proposes a classification framework for multisource remote sensing image fusion. First, a spatial and spectral feature projection network is constructed based on parallel feature extraction by combining HSI and LiDAR data, which is conducive to extracting joint spatial, spectral, and elevation features from different source data. Furthermore, in order to construct local–global nonlinear feature mapping more flexibly, a network architecture coupling together multiscale convolution and a multiscale vision transformer is proposed. Moreover, a plug-and-play nonlocal feature token aggregation module is designed to adaptively adjust the domain offsets between different features, while a class token is employed to reduce the complexity of high-dimensional feature fusion. On three open-source remote sensing datasets, the performance of the proposed multisource fusion classification framework improves about 1% to 3% over other state-of-the-art algorithms.

Hyperspectral Image Classification on Large-Scale Agricultural Crops: The Heilongjiang Benchmark Dataset, Validation Procedure, and Baseline Results

Over the past few decades, researchers have shown sustained and robust investment in exploring methods for hyperspectral image classification (HSIC). The utilization of hyperspectral imagery (HSI) for crop classification in agricultural areas has been widely demonstrated for its feasibility, flexibility, and cost-effectiveness. However, numerous coexisting issues in agricultural scenarios, such as limited annotated samples, uneven distribution of crops, and mixed cropping, could not be explored insightfully in the mainstream datasets. The limitations within these impractical datasets have severely restricted the widespread application of HSIC methods in agricultural scenarios. A benchmark dataset named Heilongjiang (HLJ) for HSIC is introduced in this paper, which is designed for large-scale crop classification. For practical applications, the HLJ dataset covers a wide range of genuine agricultural regions in Heilongjiang Province; it provides rich spectral diversity enriched through two images from diverse time periods and vast geographical areas with intercropped multiple crops. Simultaneously, considering the urgent demand of deep learning models, the two images in the HLJ dataset have 319,685 and 318,942 annotated samples, along with 151 and 149 spectral bands, respectively. To validate the suitability of the HLJ dataset as a baseline dataset for HSIC, we employed eight classical classification models in fundamental experiments on the HLJ dataset. Most of the methods achieved an overall accuracy of more than 80% with 10% of the labeled samples used for training. Furthermore, the advantages of the HLJ dataset and the impact of real-world factors on experimental results are comprehensively elucidated. The comprehensive baseline experimental evaluation and analysis affirm the research potential of the HLJ dataset as a large-scale crop classification dataset.

Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images

Pine wilt disease (PWD) is a destructive disease of pine trees caused by the pine wood nematode and early identification is crucial for disease control. Hyperspectral drone imagery has the potential to achieve early detection, although specific methods have not been sufficiently explored, including the spectral characteristics of early infections and the most efficient identification methods. This study aimed to examine the spectral responses to early infection, quantify the separability of healthy and early infections, and compare the accuracy and efficiency of different methods including single bands, vegetation indices (VIs) and 1st derivative reflectance and indices. We collected hyperspectral drone data in southeast China and used linear discriminant analysis (LDA) to determine the separability of healthy trees and trees at an early stage of infection. We also used bands with the same wavelengths as Sentienl-2 images (denoted S2 bands) to propose a standard for band selection.We found that it was possible to separate healthy trees and those at an early stage of infection around 0.71–0.78 using different methods. Among individual bands, the red-edge bands had the highest separability of 0.74. Using standard vegetation indices resulted in separability between 0.67 and 0.71; in addition, we proposed three new indices that achieved separability between 0.73 and 0.75. The 1st derivative reflectance at 714 nm had the highest separability of 0.78 in this study, while using the 1st derivative reflectance indices was slightly less accurate. The classification accuracy was also slightly lower when using Random Forest (RF) with all bands, sensitive bands, and S2 bands.We conclude that, of the methods tested, the red-edge bands are most sensitive to early infection, and using the 1st derivative reflectance at 714 nm or using the 1st derivative reflectance at the red-edge and blue-edge inflection point (REIP and BEIP) was sufficient for early identification. Acquiring and using additional wavelengths hardly improved the classification. Using wavelengths similar to those in the Sentienl-2 images achieved similar results, and thus can be used as a standard for dimensionality reduction of hyperspectral data pertaining to forest disease. The newly proposed VIs and 1st derivative reflectances at the yellow-edge inflection point (YEIP) and BEIP delivered better performance than the other tested indices, and could be alternatives for early identification. This study proposes simple and practical methods for early identification of wilt and provides insights for efficient data acquisition and data reduction.

Raman and photoluminescence signal separation in Raman hyperspectral imagery including noise reduction

AbstractRaman hyperspectral imaging (RHSI) is a valuable tool for gaining crucial information about the chemical composition of materials. However, obtaining clear Raman signals is not always a trivial task. Raw Raman signals can be susceptible to photoluminescence interference and noise. Hence, the preprocessing of RHSI is a required step for an effective and reliable chemical analysis. The main challenge is splitting the measured RHSI into separate Raman photoluminescence signals. Since no golden‐standard exists, it is non‐trivial to validate the correctness of the separated signals. While current state‐of‐the‐art preprocessing methods are effective, they require expert knowledge and involve unintuitive hyperparameters. Current approaches also lack generalizability, requiring extensive hyperparameter tuning on a case‐by‐case basis, while even then results are not always as expected. To this end, this work proposes a novel iterative RHSI preprocessing pipeline for splitting raw Raman signals and noise removal based on linear spline and radial basis function regression (IlsaRBF). The proposed method involves hyperparameters based on the physical properties of Raman spectroscopy, making them intuitive to use. This leads to more robust and stable hyperparameters, reducing the necessity for extensive hyperparameter tuning. A thorough evaluation shows that the proposed method outperforms the current state‐of‐the‐art. Additionally, a cosmic ray identification and removal algorithm (CRIR) and dynamic PCA for noise reduction are introduced. A standalone tool containing our proposed methods is provided, making RHSI preprocessing available to a broader audience, aiding further research and advancements in the field of Raman spectroscopy.

PRISMA hyperspectral imagery for mapping alteration zones associated with Kuhpanj porphyry copper deposit, Southern Iran

ABSTRACT Hyperspectral images have been extensively employed to map alterations related to various ore deposits, particularly those associated with porphyry copper deposits. The present study aims to evaluate the efficiency of the PRISMA (Precursore Iper Spettraledella Missione Applicativa) satellite imagery, that is an Italian satellite, in identifying and discriminating alteration zones (Phyllic, Argillic, Propylitic) related to a well-known porphyry copper mineralization system in Iran. The chosen algorithms for performance evaluations involve selective principle component analysis (SPCA), spectral band ratios, mixture-tuned matched filtering (MTMF), and linear spectral unmixing (LSU). In this study, new band ratios are also proposed to improve the efficiency of alteration mapping. The results are validated by field data and geological-alteration map demonstrating the overall accuracy of detecting different alteration zones including the Phyllic, Argillic, and Propylitic zones equal to 71.87%, 87.50%, 81.25% and 71.87%, and kappa coefficients of 58.85%, 80.54%, 70.55% and 57.52% for SPCA, band ratio, MTMF, and LSU algorithms, respectively. In current research, it is found that using PRISMA data, the band ratio algorithm is superior when compared to other implemented algorithms in detecting hydrothermal alteration associated with porphyry copper deposits.

Automated Hyperspectral Feature Selection and Classification of Wildlife Using Uncrewed Aerial Vehicles

Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense vegetation and under canopies. This research focused on investigating the feasibility and practicality of using uncrewed aerial systems (UAS) and hyperspectral imagery (HSI) to classify animals in the wild on a spectral—rather than spatial—basis, in the hopes of developing methods to accurately classify animal targets even when their form may be significantly obscured. We collected HSI of four species of large mammals reported as invasive species on islands: cow (Bos taurus), horse (Equus caballus), deer (Odocoileus virginianus), and goat (Capra hircus) from a small UAS. Our objectives of this study were to (a) create a hyperspectral library of the four mammal species, (b) study the efficacy of HSI for animal classification by only using the spectral information via statistical separation, (c) study the efficacy of sequential and deep learning neural networks to classify the HSI pixels, (d) simulate five-band multispectral data from HSI and study its effectiveness for automated supervised classification, and (e) assess the ability of using HSI for invasive wildlife detection. Image classification models using sequential neural networks and one-dimensional convolutional neural networks were developed and tested. The results showed that the information from HSI derived using dimensionality reduction techniques were sufficient to classify the four species with class F1 scores all above 0.85. The performances of some classifiers were capable of reaching an overall accuracy over 98%and class F1 scores above 0.75, thus using only spectra to classify animals to species from existing sensors is feasible. This study discovered various challenges associated with the use of HSI for animal detection, particularly intra-class and seasonal variations in spectral reflectance and the practicalities of collecting and analyzing HSI data over large meaningful areas within an operational context. To make the use of spectral data a practical tool for wildlife and invasive animal management, further research into spectral profiles under a variety of real-world conditions, optimization of sensor spectra selection, and the development of on-board real-time analytics are needed.

FPGA-based remote target classification in hyperspectral imaging using multi-graph neural network

Hyperspectral imagery (HSI) is widely used in remote sensing for target classification; however, its accurate classification remains challenging due to the scarcity of labeled data. Graph Neural Networks (GNNs) have emerged as a popular method for semi-supervised classification, attracting significant interest in the context of HSI analysis. Nevertheless, conventional GNN-based approaches often rely on a single graph filter to extract HSI characteristics, failing to fully exploit the potential benefits of different graph filters. Additionally, oversmoothing issues plague classical GNNs, further affecting classification performance. To address these drawbacks, we propose a novel approach called Spectral and Autoregressive Moving Average Graph Filter for the Multi-Graph Neural Network (SAM-GNN). This approach leverages two distinct graph filters: one specialized in extracting the spectral characteristics of nodes and the other effectively suppressing graph distortion. Through extensive evaluations, we compare the performance of SAM-GNN with other state-of-the-art methods, employing metrics such as overall accuracy (OA), individual class accuracy (IA), and Kappa coefficient (KC). The results shows that the SAM-GNN provides an improvement in KC, IA, and OA of 6.71%, 5.7%, and 3.93% for the Pavia University dataset and 4.67%, 3.67%, and 3.49% for the Cuprite dataset respectively. Furthermore, we implement SAM-GNN on the Virtex-7 field-programmable gate array (FPGA), demonstrating that the method achieves highly accurate target localization results, bringing us closer to real-world applications in HSI classification.

Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland

Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire regions, albeit susceptible to external environmental interference. This study, conducted in the Yuncheng Basin of the Yellow River Basin, compared the predictive accuracy of laboratory hyperspectral data (ASD FieldSpec4) and GF-5 satellite hyperspectral imagery for SOC mapping. Leveraging fractional order derivatives (FODs), various denoising methods, feature band selection, and the Random Forest model, the research revealed that laboratory hyperspectral data outperform satellite data in predicting SOC. FOD processing enhanced spectral information, and discrete wavelet transform (DWT) proved effective for GF-5 satellite imagery denoising. Stability competitive adaptive re-weighted sampling (sCARS) emerged as the optimal feature band selection algorithm. The 0.6FOD-sCARS RF model was identified as the optimal laboratory hyperspectral prediction model for SOC, while the 0.8FOD-DWT-sCARS RF model was deemed optimal for satellite hyperspectral prediction. This research, offering insights into farmland soil quality monitoring and strategies for sustainable soil use, holds significance for enhancing agricultural production efficiency.

Appraisal of EnMAP hyperspectral imagery use in LULC mapping when combined with machine learning pixel-based classifiers

The recent availability of satellite hyperspectral imaging combined with the developments in the classification techniques have paved the way towards improving our ability to obtain information on the spatiotemporal distribution of land use/land cover (LULC) at improved accuracy. In this context, the present study aims at evaluating the combined use of the recently launched Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite mission with two powerful machine learning (ML) classifiers. In particular, the Support Vector Machines (SVM) and Random Forest (RF) are used synergistically with EnMAP imagery in performing LULC mapping at a typical Mediterranean setting located in northern Greece. Evaluation of the derived LULC maps is based on the computation of a series classification accuracy metrics. The McNemar's chi-square statistical significance testing was also computed to confirm the statistical significance of the differences between the classifiers. In overall, results showed that SVM slightly outperformed RF, exhibiting a higher overall accuracy, of 92.6% and 88.1%, respectively, whereas the statistical significance of the findings was also attested by the McNemar's statistical test results. To our knowledge, this study is one of the first published so far focusing on exploring the capabilities of ENMAP imagery when combined with different ML pixel-based classifiers in the context of LULC mapping. Our results, indeed provided useful insights on the potential of EnMAP datasets in deriving information on the spatiotemporal distribution of LULC at a highly fragmented Mediterranean landscape, evidencing the EnMAP promising potential in this field.

Hyperspectral Imaging for Determining Reflection Variables in Chilli Leaves Infested with Green Peach Aphid

Atshan, L.A., H.A. Ibrahim, K.J. Abdulrazzaq and K.A. Atshan. 2024. Hyperspectral Imaging for Determining Reflection Variables in Chilli Leaves Infested with Green Peach Aphid. Arab Journal of Plant Protection, 42(3): 299-305. https://doi.org/10.22268/AJPP-001256 Aphid infestations cause a physiological response detectable by a remote leaf reflectance sensor. Changes in the spectral signature of specific wave bands, measured with hyperspectral imagery (HI), may also relate to the absence, presence and/or level of infestation of aphids. Aphids cause significant damage to crops and yields in the field as well as in greenhouses. Green peach aphid, Myzus persicae (Sulzer) destroy chloroplast cells; this damage can be spectrally detected in the reflectance of the visible and near-infrared (NIR) regions. The spectral curve showed that the aphid-infested chili leaves reflectance in the NIR, decreased in time with the increase of aphid population. Although the aphid’s activity occurred on the leaves underside, their damage can be spectrally detected by reflection data from the upper side. Early aphid damage was identified in limited areas of chili leaf and this damage showed to be the only harm inflicted on the plant, also proven by the HI data obtained. Surface reflectance (%) from un-infested chili was lower in the visible and higher in the NIR light 90A spectrum when compared with aphids-infested chili. The overall classification accuracies of 89% for damage detection were achieved. These results indicated that HI can be effectively used to accurately detect and quantify aphid infestation in chili for site-specific aphid management. It can be potentially applied for limited areas as well as fields as an early detection tool for aphid management. This study aimed to spectrally explore the ability to assess the level of aphids damage in a limited area grown with the chili crop. Keywords: Hyperspectral imagery, aphids, chilli pepper, near-infrared

Target Detection With Spectral Graph Contrast Clustering Assignment and Spectral Graph Transformer in Hyperspectral Imagery

Target Detection With Spectral Graph Contrast Clustering Assignment and Spectral Graph Transformer in Hyperspectral Imagery

A Hybrid convolution neural network for the classification of tree species using hyperspectral imagery.

In recent years, the advancement of hyperspectral remote sensing technology has greatly enhanced the detailed mapping of tree species. Nevertheless, delving deep into the significance of hyperspectral remote sensing data features for tree species recognition remains a challenging endeavor. The method of Hybrid-CS was proposed to addresses this challenge by synergizing the strengths of both deep learning and traditional learning techniques. Initially, we extract comprehensive correlation structures and spectral features. Subsequently, a hybrid approach, combining correlation-based feature selection with an optimized recursive feature elimination algorithm, identifies the most valuable feature set. We leverage the Support Vector Machine algorithm to evaluate feature importance and perform classification. Through rigorous experimentation, we evaluate the robustness of hyperspectral image-derived features and compare our method with other state-of-the-art classification methods. The results demonstrate: (1) Superior classification accuracy compared to traditional machine learning methods (e.g., SVM, RF) and advanced deep learning approaches on the tree species dataset. (2) Enhanced classification accuracy achieved by incorporating SVM and CNN information, particularly with the integration of attention mechanisms into the network architecture. Additionally, the classification performance of a two-branch network surpasses that of a single-branch network. (3) Consistent high accuracy across different proportions of training samples, indicating the stability and robustness of the method. This study underscores the potential of hyperspectral images and our proposed methodology for achieving precise tree species classification, thus holding significant promise for applications in forest resource management and monitoring.

A Comprehensive BRF Model for SpectralonⓇ and Application to Hyperspectral Field Imagery

A Comprehensive BRF Model for SpectralonⓇ and Application to Hyperspectral Field Imagery

Underground Natural Gas Microleakage Detection With Hyperspectral Imagery Based on Temporal Features and Ensemble Learning

Underground Natural Gas Microleakage Detection With Hyperspectral Imagery Based on Temporal Features and Ensemble Learning

Bipartite Graph-Based Projected Clustering With Local Region Guidance for Hyperspectral Imagery

Bipartite Graph-Based Projected Clustering With Local Region Guidance for Hyperspectral Imagery

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments.

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

Remote Sensing Techniques: Hyperspectral Imaging and Data Analysis.

Hyperspectral imaging is a remote sensing technique that enables remote, noninvasive measurement of plant traits. Here, we outline the procedures for camera setup, scanning, and calibration, along with the acquisition of black and white reference materials, which are the key steps in collecting hyperspectral imagery. We also discuss the development of predictive models such as partial least-squares regression, using both large and small datasets, which are used to predict plant traits from hyperspectral data. To ensure practical applicability, we provide code examples that allow readers to immediately implement these techniques in real-world scenarios. We introduce these topics to beginners in an accessible and understandable manner.