Spectral Imaging Technology Research Articles

Goji Disease and Pest Monitoring Model Based on Unmanned Aerial Vehicle Hyperspectral Images

Combining near-earth remote sensing spectral imaging technology with unmanned aerial vehicle (UAV) remote sensing sensing technology, we measured the Ningqi No. 10 goji variety under conditions of health, infestation by psyllids, and infestation by gall mites in Shizuishan City, Ningxia Hui Autonomous Region. The results indicate that the red and near-infrared spectral bands are particularly sensitive for detecting pest and disease conditions in goji. Using UAV-measured data, a remote sensing monitoring model for goji pest and disease was developed and validated using near-earth remote sensing hyperspectral data. A fully connected neural network achieved an accuracy of over 96.82% in classifying gall mite infestations, thereby enhancing the precision of pest and disease monitoring in goji. This demonstrates the reliability of UAV remote sensing. The pest and disease remote sensing monitoring model was used to visually present predictive results on hyperspectral images of goji, achieving data visualization.

Retraction Note: Application of intelligent trajectory analysis based on new spectral imaging technology in basketball match motion recognition

Retraction Note: Application of intelligent trajectory analysis based on new spectral imaging technology in basketball match motion recognition

An Improved Hyper Spectral Imaging for Accurate Disease Diagnosis in Sustainable Medical Environments

Hyper spectral imaging (HSI) has emerged as a powerful technique for accurate disease diagnosis in medical environments. It provides high-resolution images with detailed spectral information, making it possible to identify subtle differences between healthy and diseased tissues. However, current HSI systems face challenges in terms of accuracy and efficiency, limiting their widespread application in sustainable medical environments. To overcome these challenges, our team has developed an improved HSI system that utilizes state-of-the-art spectral imaging technology and machine learning algorithms. This system is capable of capturing and analyzing a wider range of spectral data, enabling more precise identification of disease-specific spectral signatures. Furthermore, our system has been optimized to operate with minimal power consumption, making it environmentally friendly and suitable for sustainable medical environments. The improved HSI system has been successfully tested in clinical settings and has shown promising results in accurately diagnosing various diseases such as cancer, dermatitis, and cardiovascular conditions. Its high accuracy and fast processing time make it a valuable tool for early disease detection and treatment planning. Moreover, the ability to operate with low energy consumption makes it a sustainable solution for medical facilities in resourcelimited areas. In addition to its accuracy and efficiency, our improved HSI system is also user-friendly and can be easily integrated into existing medical imaging systems.

Feasibility and potential of terahertz spectral and imaging technology for Apple Valsa canker detection: A preliminary investigation

Apple Valsa canker (AVC) caused by the Ascomycete Valsa mali, seriously constrains the production and quality of apple fruits. The symptomless incubation characteristics of Valsa mali make it highly challenging to detect AVC at an early infection stage. After infecting the wound of apple bark, the pathogenic hyphae of AVC will expand and colonize the phloem tissue. Meanwhile, various enzymes and toxic substances released by hyphae cause the decomposition of cellulose and lignin, and the generation of poisonous secondary metabolites in bark tissue. However, these early symptoms of AVC are invisible from the bark’s appearance. Fortunately, Terahertz Spectral Imaging (ThzSI) technology with the advantage of penetrating, and fingerprinting is promising for detecting hidden or slight symptoms of the fungal infection. This study is a preliminary investigation of terahertz frequency-domain spectra for AVC in the early stage of infection. Healthy and two-week-infected apple tree branches were prepared for capturing ThzS images, and the spectral data were preprocessed by Multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing (SG), and standard normal variate (SNV) respectively to remove data noise and improve data quality. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and random frog (RFROG) were employed to extract the spectral feature bands to eliminate redundant data and improve computational efficiency. Machine learning models were established based on the spectral features to detect AVC at an early infection stage, where 11 of them exhibited the best performance with F1-score of 99.72%. To further explore disease information in spatial spectra, imaging data were acquired using terahertz imaging technology. Based on imaging data, pseudo-color imaging, histogram equalization, and Otsu segmentation were employed to visualize early infection areas in apple barks. Furthermore, histogram feature (HF), shape feature (SF), and local binary pattern (LBP) extracted from terahertz spectral images were utilized to establish the SVM, RF, and KNN models. HF-SF-KNN and HF-SF-LBP-KNN with the best performance achieved F1-score of 98.82%. This study presents a preliminary application of terahertz spectral and imaging technology for early-stage AVC detection and demonstrates its feasibility. Additionally, it provides a new way to detect AVC, which expands the application of ThzSI technology in tree disease detection in orchards and lays the foundation for further research.

Research on Non-Destructive Quality Detection of Sunflower Seeds Based on Terahertz Imaging Technology.

The variety and content of high-quality proteins in sunflower seeds are higher than those in other cereals. However, sunflower seeds can suffer from abnormalities, such as breakage and deformity, during planting and harvesting, which hinder the development of the sunflower seed industry. Traditional methods such as manual sensory and machine sorting are highly subjective and cannot detect the internal characteristics of sunflower seeds. The development of spectral imaging technology has facilitated the application of terahertz waves in the quality inspection of sunflower seeds, owing to its advantages of non-destructive penetration and fast imaging. This paper proposes a novel terahertz image classification model, MobileViT-E, which is trained and validated on a self-constructed dataset of sunflower seeds. The results show that the overall recognition accuracy of the proposed model can reach 96.30%, which is 4.85%, 3%, 7.84% and 1.86% higher than those of the ResNet-50, EfficientNeT, MobileOne and MobileViT models, respectively. At the same time, the performance indices such as the recognition accuracy, the recall and the F1-score values are also effectively improved. Therefore, the MobileViT-E model proposed in this study can improve the classification and identification of normal, damaged and deformed sunflower seeds, and provide technical support for the non-destructive detection of sunflower seed quality.

A multiscale dilated attention network for hyperspectral image classification

Hyperspectral imaging is an image obtained by combining spectral detection technology and imaging technology, which can collect electromagnetic spectra in the wavelength range of visible light to near-infrared. It is an important research content in the field of ground observation in hyperspectral remote sensing. However, hyperspectral image face significant challenges in classification task due to their high spectral dimensions, lack of labeled samples, and strong correlation between bands. In order to fully extract features from both spectral and spatial dimensions and improve classification accuracy in the case of limited training samples, a multiscale dilated attention network is proposed for hyperspectral image classification. First, a three-dimensional convolutional layer is used to extract the shallow features of the image. Then, a multiscale dilated attention module is proposed by combining dilated convolution and channel attention. Using ordinary convolution and dilated convolution to form different receptive fields. Channel attention is used to remodel the obtained multiscale features, enhancing the inter-channel correlation. After that, a multiscale spatial-spectral attention module is constructed using multiple asymmetric convolutions to obtain spatial and spectral attention features at different positions, further enhancing important feature suppression over non-important features. Finally, using softmax to classify the obtained features. Using Indian Pines, Pavia University, KSC and University of Houston as experimental datasets, the overall classification accuracy of this paper’s method achieved 98.97%, 99.14%, 99.45%, and 98.56% respectively, using only 5%, 1%, 10%, and 10% of training samples per class. Compared with seven advanced classification methods, the experimental results show that the proposed method can achieve the highest classification accuracy with limited training samples.

Identification and sorting of impurities in tea using spectral vision

Removing impurities from Puer tea is an important step in its processing. However, existing methods are unable to accurately and efficiently sort impurities from Puer tea. This study proposes a novel method based on spectral imaging technology to identify impurities in Puer tea. Hyperspectral images of tea and impurities were acquired in the 400–1000 nm wavelengths. The spectra of each category of samples were extracted from the hyperspectral images and comprehensively analysed and processed, mainly including outlier detection and elimination, spectral preprocessing, and characteristic wavelengths selection. Support vector machine (SVM) models are built based on full-spectrum data and characteristic wavelengths data to achieve pixel-level classification of hyperspectral images. The results showed that the best model based on full spectra achieved an accuracy of 97.8% on the test dataset. The best models based on characteristic wavelengths selected by successive projections algorithm and genetic algorithm achieved accuracies of 95.9% and 94.9%, respectively. Finally, these pixel classification models were applied to the test samples to test the effectiveness of the models in identifying impurities. The results indicate that the three selected models can effectively identify impurities and even perform well on identifying unfamiliar categories of impurities.

Non-destructive Banana Quality Assessment and Quality and Safety Monitoring using Spectral Imaging Technology

This thesis discusses a method to realize non-destructive banana quality assessment and quality and safety monitoring using spectral imaging technology. As one of the important agricultural products in China, the quality and safety of bananas have always attracted much attention. Traditional quality assessment methods often require destroying bananas, but this method uses spectral imaging technology to realize the assessment of banana quality by measuring and analyzing the spectral characteristics of bananas. At the same time, this method also utilizes spectroscopic technology to detect harmful substances in bananas to realize the safety monitoring of banana quality. The experimental results show that the method has high accuracy and reliability, and can be used as a rapid, efficient, non-destructive means of banana quality assessment and quality and safety monitoring.

Spectroscopy Detection and Imaging System Based on Line Array Single Photon Detectors

Abstract. Single-photon detectors, with their exceptional sensitivity, provide a reliable means for single-photon-level detection, demonstrating significant advantages in detecting weak signals in complex environments compared to traditional detectors. With the continuous advancement in semiconductor manufacturing technology, single-photon detectors based on linear array configurations have emerged and rapidly developed. This study utilizes a linear array single-photon detector in free-running mode combined with a scanning mechanism to design and implement a spectral detection and imaging system. Through the spectral scanning unit, this system successfully achieves precise spectral detection in the 890 nm to 1710 nm range, with a spectral resolution better than 2 nm. Utilizing the imaging scanning unit, the system effectively performs target spectral imaging under single and multiple wavelength conditions at 1064 nm, 1310 nm, and 1520 nm. By optimizing algorithms for data processing, the system can achieve rapid and accurate spectral detection and imaging even under low-light conditions where the average photon count per pixel is less than 3. The results of this study are expected to provide strong technical support for the application of spectral imaging technology in the field of high-speed detection and imaging.

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

Spectral computed tomography (CT)-reconstructed images often exhibit severe noise and artifacts, which compromise the practical application of spectral CT imaging technology. Methods that use tensor dictionary learning (TDL) have shown superior performance, but it is difficult to obtain a high-quality pre-trained global tensor dictionary in practice. In order to resolve this problem, this paper develops an algorithm called tensor decomposition with total generalized variation (TGV) for sparse-view spectral CT reconstruction. In the process of constructing tensor volumes, the proposed algorithm utilizes the non-local similarity feature of images to construct fourth-order tensor volumes and uses Canonical Polyadic (CP) tensor decomposition instead of pre-trained tensor dictionaries to further explore the inter-channel correlation of images. Simultaneously, introducing the TGV regularization term to characterize spatial sparsity features, the use of higher-order derivatives can better adapt to different image structures and noise levels. The proposed objective minimization model has been addressed using the split-Bregman algorithm. To assess the performance of the proposed algorithm, several numerical simulations and actual preclinical mice are studied. The final results demonstrate that the proposed algorithm has an enormous improvement in the quality of spectral CT images when compared to several existing competing algorithms.

Discriminating Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Review.

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral-spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral-spatial representations for classification. In this article, the crucial factors to discriminate spectral-spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral-spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.

Spectral imaging based on custom-made multi-strip filter arrays

Spectral imaging technology based on on-chip spectroscopy can find applications in areas including aerospace, industrial and consumer electronics, and so on. Since each application normally requires a different set and number of spectral bands, the development of customized spectroscopy solutions with more compact size and lower cost becomes quite important. In this paper, we demonstrate a compact, highly customizable imaging spectrometer scheme based on custom-made multi-strip filter arrays, which maintains an average high transmission of ∼85%, narrow bandwidth of ∼30nm, and high optical density of ∼OD2 in the blocking regions across the visible to near-infrared waveband. Spectral imaging experiments are conducted, and the accurate reconstruction of sparse spectral image data is demonstrated as well to prove the validity of the proposed scheme. As a result, the work reported in this paper allows researchers to develop customized spectral imaging equipment in a relatively easy way and also has a great potential to be engineered further for scalable production with a quite low cost.

Spectral line confocal sensor signal analysis and correction: Unlocking reflectance difference sample measurements

Spectral line confocal imaging (LCI) technology enables high-resolution, rapid 3D surface morphology analysis of transparent materials and other samples, positioning it as a leading optical measurement tool. Nevertheless, where the sensor measures samples with significant variations in surface reflectance, the peak signals of the acquired images exhibit low signal-to-noise ratios or become distorted, hampering the peak extraction algorithm from accurately locating their peak positions. Consequently, it's arduous to reconstruct their precise surface topography. To enhance the sensor's dynamic measurement range and adaptability, this paper introduces an adaptive correction approach. It utilizes a regularized non-negative matrix to decompose images with various grey levels. This is followed by weighted frequency filtering and Gaussian enhancement of all the grey layers. Eventually, these layers are fused into one image with high contrast and uniformity. Using this approach, the sensor is capable of achieving precise 3D reconstruction with high-quality peak signals. Additionally, this study experimentally demonstrates the efficacy of the suggested approach by measuring the interdigital electrode and printed circuit boards (PCB). It should be noted that the technique presented in this paper is not limited to spectral line confocal instruments and is also effective for line laser and line structured light instruments.

Low-rank reconstruction for mid-infrared spectroscopy using broadband ultra-wide-angle photonic filters and IReg algorithm

In this study, a broadband mid-infrared photonic filter is designed. It has the advantages of polarization insensitivity, ultrawide angle, high manufacturability, and easy integration. For infrared spectrum reconstruction, the use of filters as spectral sampling channels can eliminate the influence of slits or complex optical splitting elements in spectral imaging technology. Further, we propose a low-rank matrix logarithm regularization algorithm based on a three-segment filter function (IReg algorithm) combined with the designed filter for a variety of mid-infrared detector front ends, which can significantly suppress noise interference. Here, we demonstrate that the stability of the spectral reconstruction accuracy under the large disturbance environment obtained by the IReg algorithm is much higher than those of the L2 and L1 algorithms. In the low-dimensional matrix state, the accuracy and stability of the ill-conditioned linear equations are guaranteed, the anti-noise ability is enhanced, the effect of data dimensionality reduction is realized, and the accuracy of mid-infrared spectral reconstruction is improved.

Snapshot spectral imaging: from spatial-spectral mapping to metasurface-based imaging

Abstract Snapshot spectral imaging technology enables the capture of complete spectral information of objects in an extremely short period of time, offering wide-ranging applications in fields requiring dynamic observations such as environmental monitoring, medical diagnostics, and industrial inspection. In the past decades, snapshot spectral imaging has made remarkable breakthroughs with the emergence of new computational theories and optical components. From the early days of using various spatial-spectral data mapping methods, they have evolved to later attempts to encode various dimensions of light, such as amplitude, phase, and wavelength, and then computationally reconstruct them. This review focuses on a systematic presentation of the system architecture and mathematical modeling of these snapshot spectral imaging techniques. In addition, the introduction of metasurfaces expands the modulation of spatial-spectral data and brings advantages such as system size reduction, which has become a research hotspot in recent years and is regarded as the key to the next-generation snapshot spectral imaging techniques. This paper provides a systematic overview of the applications of metasurfaces in snapshot spectral imaging and provides an outlook on future directions and research priorities.

Research on the Effect of Vibrational Micro-Displacement of an Astronomical Camera on Detector Imaging

Scientific-grade cameras are frequently employed in industries such as spectral imaging technology, aircraft, medical detection, and astronomy, and are characterized by high precision, high quality, fast speed, and high sensitivity. Especially in the field of astronomy, obtaining information about faint light often requires long exposure with high-resolution cameras, which means that any external factors can cause the camera to become unstable and result in increased errors in the detection results. This paper aims to investigate the effect of displacement introduced by various vibration factors on the imaging of an astronomical camera during long exposure. The sources of vibration are divided into external vibration and internal vibration. External vibration mainly includes environmental vibration and resonance effects, while internal vibration mainly refers to the vibration caused by the force generated by the refrigeration module inside the camera during the working process of the camera. The cooling module is divided into water-cooled and air-cooled modes. Through the displacement and vibration experiments conducted on the camera, it is proven that the air-cooled mode will cause the camera to produce greater displacement changes relative to the water-cooled mode, leading to blurring of the imaging results and lowering the accuracy of astronomical detection. This paper compares the effects of displacement produced by two methods, fan cooling and water-circulation cooling, and proposes improvements to minimize the displacement variations in the camera and improve the imaging quality. This study provides a reference basis for the design of astronomical detection instruments and for determining the vibration source of cameras, which helps to promote the further development of astronomical detection.

Balancing composite motion optimization using R-ERNN with plant disease

Soybean is a crop with a long cultivation history that plays a significant role on agricultural field. Early diagnosis of Soybean Mosaic Virus Disease (SMVD) is must require, because it causes a fast reduction and significant losses in soybean yields. Therefore, this paper proposes a Balancing Composite Motion optimization utilizing Recalling-Enhanced Recurrent Neural Network with Plant Disease for separating its severity into 0, 1 and 2 grades. Initially, hyper spectral image data was taken from Spec View software. Then, the spectral imageries of soybean leaves are processed to remove the redundancy in frequency bands that emerges owing to hyper spectral camera does not present any important information are filtered by Dual Tree Complex Wavelet Transform (DTCWT). In this, hyper spectral image features are extracted using Ternary pattern and discrete wavelet transform (TP-DWT) method. After completing the process of feature extraction, the feature extracted imageries are given to Recalling-Enhanced Recurrent Neural Network (RE-RNN) and it is used to classify the Soybean mosaic disease. Here, Balancing Composite Motion Optimization (BCMO) algorithm is employed for tuning the RE-RNN hyper parameters. The proposed PDI-RE-RNN-BCMO-HSI method provides 27.03%, 28.94% and 39.04% higher precision compared with existing method like grading model of soybean mosaic infection depending upon hyper spectral imaging technology (PDI-CNN-SVM-HSI), recognization of soybean varieties under hyper spectral imaging technology and one-dimensional CNN (PDI-1D-CNN-HSI) and hyper spectral imaging technology combined with multivariate models to recognize soybean disease (PDI-SNV-ELM-HSI) respectively.

Application of Spectral Imaging Technology in Field of Forensic Science

Application of Spectral Imaging Technology in Field of Forensic Science

RETRACTED ARTICLE: Application of intelligent trajectory analysis based on new spectral imaging technology in basketball match motion recognition

RETRACTED ARTICLE: Application of intelligent trajectory analysis based on new spectral imaging technology in basketball match motion recognition

Estimation of Apple Leaf Nitrogen Concentration Using Hyperspectral Imaging-Based Wavelength Selection and Machine Learning

In apple cultivation, the total nitrogen content is an important indicator of plant growth, fruit quality, and yield. Timely monitoring of growth becomes imperative, since an imbalance, either in deficiency or excess nitrogen, can result in physiological disorders, adversely impacting both the quantity and quality of fruit. Leaf nitrogen content can be determined using simple chlorophyll meters or destructive testing; however, these methods are time-consuming. However, by employing spectral imaging technology, it is possible to swiftly predict leaf nitrogen content. This study estimated the total nitrogen content in apple trees via hyperspectral imaging and machine learning-based regression analysis (partial least-squares regression (PLSR), support vector regression (SVR), and eXtreme gradient boosting regression (XGBoost). Additionally, to reduce computational costs and improve reproducibility, spectral binning was divided into three stages (4, 8, and 16 bins), and models were compared with a 2-binning estimation model. The analysis focused on green, red, red edge, and near-infrared (NIR) spectra, with 5–10 selected wavelengths, and the SVR-based prediction model showed a similar or greater performance to that of the full spectrum. At 4- and 8-binning, the selected wavelengths were similar to those at 2-binning, maintaining similar prediction model performance. However, at 16 bp, the performance of the prediction model decreased owing to spectral data loss, leading to a significant reduction in wavelengths for nitrogen content estimation. These results can support informed nitrogen fertilization decisions, enabling precise, real-time monitoring of nitrogen content for enhanced plant growth, fruit quality, and yield in apple trees. Additionally, the selected wavelengths can be considered in the development of new types of multispectral sensors.